diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index b9c3489..7120cfa 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -39,6 +39,8 @@ jobs:
           go-version: "1.24"
       - name: Build with CGO (llamacpp tag)
         run: CGO_ENABLED=1 go build -tags llamacpp ./ggml/llamacpp/...
+      - name: Build with CGO (whispercpp tag)
+        run: CGO_ENABLED=1 go build -tags whispercpp ./ggml/whispercpp/...
 
   build-libs:
     name: Build Libraries from Source (${{ matrix.os }})
@@ -68,9 +70,16 @@ jobs:
       - name: Build static libraries from source
         run: make build-libs
       - name: Verify CGO build with fresh libraries
-        run: CGO_ENABLED=1 go build -tags llamacpp ./ggml/llamacpp/...
-      - name: Upload prebuilt artifacts
+        run: |
+          CGO_ENABLED=1 go build -tags llamacpp ./ggml/llamacpp/...
+          CGO_ENABLED=1 go build -tags whispercpp ./ggml/whispercpp/...
+      - name: Upload llama.cpp prebuilt artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: prebuilt-llamacpp-${{ matrix.platform }}
+          path: ggml/llamacpp/third_party/prebuilt/${{ matrix.platform }}/*.a
+      - name: Upload whisper.cpp prebuilt artifacts
         uses: actions/upload-artifact@v4
         with:
-          name: prebuilt-${{ matrix.platform }}
-          path: third_party/llama.cpp/prebuilt/${{ matrix.platform }}/*.a
+          name: prebuilt-whispercpp-${{ matrix.platform }}
+          path: ggml/whispercpp/third_party/prebuilt/${{ matrix.platform }}/*.a
diff --git a/.gitignore b/.gitignore
index de68be1..9edbd7d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,7 @@
 # Build artifacts (source downloads, cmake dirs)
-third_party/llama.cpp/src/
-third_party/whisper.cpp/src/
-third_party/**/build/
+ggml/llamacpp/third_party/src/
+ggml/whispercpp/third_party/src/
+**/build/
 out/
 
 # Prebuilt .a files and headers ARE committed — do not ignore them
diff --git a/Dockerfile.libs b/Dockerfile.libs
index eab9cee..7e7fbce 100644
--- a/Dockerfile.libs
+++ b/Dockerfile.libs
@@ -1,4 +1,4 @@
-# Dockerfile.libs — build linux-amd64 static libraries for llama.cpp
+# Dockerfile.libs — build linux-amd64 static libraries for llama.cpp and whisper.cpp
 #
 # Usage:
 #   docker build -f Dockerfile.libs -o ./out .
@@ -18,7 +18,7 @@ COPY go.mod ./
 COPY version.go ./
 COPY cmd/versioncmd/ ./cmd/versioncmd/
 
-# Read versions and download sources
+# Download llama.cpp
 RUN LLAMA_VERSION=$(go run ./cmd/versioncmd llama.cpp) && \
     echo "Downloading llama.cpp ${LLAMA_VERSION}..." && \
     wget -qO llama.cpp.tar.gz "https://github.com/ggerganov/llama.cpp/archive/refs/tags/${LLAMA_VERSION}.tar.gz" && \
@@ -26,12 +26,25 @@ RUN LLAMA_VERSION=$(go run ./cmd/versioncmd llama.cpp) && \
     tar xzf llama.cpp.tar.gz --strip-components=1 -C llama-src && \
     rm llama.cpp.tar.gz
 
+# Download whisper.cpp
+RUN WHISPER_VERSION=$(go run ./cmd/versioncmd whisper.cpp) && \
+    echo "Downloading whisper.cpp ${WHISPER_VERSION}..." && \
+    wget -qO whisper.cpp.tar.gz "https://github.com/ggerganov/whisper.cpp/archive/refs/tags/${WHISPER_VERSION}.tar.gz" && \
+    mkdir -p whisper-src && \
+    tar xzf whisper.cpp.tar.gz --strip-components=1 -C whisper-src && \
+    rm whisper.cpp.tar.gz
+
 # Build llama.cpp
 RUN cd llama-src && \
     cmake -B build -DBUILD_SHARED_LIBS=OFF && \
     cmake --build build --config Release -j$(nproc)
 
-# Collect artifacts
+# Build whisper.cpp
+RUN cd whisper-src && \
+    cmake -B build -DBUILD_SHARED_LIBS=OFF && \
+    cmake --build build --config Release -j$(nproc)
+
+# Collect llama.cpp artifacts
 RUN mkdir -p /out/llama.cpp/linux-amd64 /out/llama.cpp/include /out/llama.cpp/ggml/include /out/llama.cpp/common && \
     find llama-src/build -name "*.a" -exec cp {} /out/llama.cpp/linux-amd64/ \; && \
     cp llama-src/include/*.h /out/llama.cpp/include/ && \
@@ -39,6 +52,12 @@ RUN mkdir -p /out/llama.cpp/linux-amd64 /out/llama.cpp/include /out/llama.cpp/gg
     cp llama-src/common/common.h /out/llama.cpp/common/ && \
     cp llama-src/common/sampling.h /out/llama.cpp/common/
 
+# Collect whisper.cpp artifacts
+RUN mkdir -p /out/whisper.cpp/linux-amd64 /out/whisper.cpp/include /out/whisper.cpp/ggml/include && \
+    find whisper-src/build -name "*.a" -exec cp {} /out/whisper.cpp/linux-amd64/ \; && \
+    cp whisper-src/include/*.h /out/whisper.cpp/include/ && \
+    cp whisper-src/ggml/include/*.h /out/whisper.cpp/ggml/include/
+
 # Output stage — docker build -o extracts from here
 FROM scratch
 COPY --from=builder /out/ /
diff --git a/Makefile b/Makefile
index 6c07128..4ff5f7c 100644
--- a/Makefile
+++ b/Makefile
@@ -2,11 +2,13 @@
 #
 # For consumers:
 #   go get github.com/footprintai/go-nativeml
-#   go build -tags llamacpp ./...    # just works — prebuilt .a files are in the module
+#   go build -tags llamacpp ./...     # just works — prebuilt .a files are in the module
+#   go build -tags whispercpp ./...   # whisper.cpp bindings
 #
 # For maintainers (rebuild .a files from source):
 #   make build-libs              # Build all libraries for current platform
 #   make build-libs-llama        # Build llama.cpp only
+#   make build-libs-whisper      # Build whisper.cpp only
 #   make build-libs-linux        # Build linux-amd64 .a files via Docker
 #   make build-libs-all          # Build native + linux-amd64
 #   make clean                   # Remove temp build dirs (keeps prebuilt .a + headers)
@@ -21,15 +23,18 @@ PLATFORM := $(shell go env GOOS)-$(shell go env GOARCH)
 # Parallel build cores
 NPROC := $(shell if which nproc > /dev/null 2>&1; then nproc; elif [ "$$(uname)" = "Darwin" ]; then sysctl -n hw.ncpu; else echo 4; fi)
 
-# Paths
-THIRD_PARTY := third_party
-LLAMA_DIR := $(THIRD_PARTY)/llama.cpp
-LLAMA_SRC := $(LLAMA_DIR)/src
-LLAMA_PREBUILT := $(LLAMA_DIR)/prebuilt/$(PLATFORM)
+# Paths — assets live inside the Go package directories
+LLAMA_THIRD_PARTY := ggml/llamacpp/third_party
+LLAMA_SRC := $(LLAMA_THIRD_PARTY)/src
+LLAMA_PREBUILT := $(LLAMA_THIRD_PARTY)/prebuilt/$(PLATFORM)
 
-.PHONY: build-libs build-libs-llama build-libs-linux build-libs-all clean verify
+WHISPER_THIRD_PARTY := ggml/whispercpp/third_party
+WHISPER_SRC := $(WHISPER_THIRD_PARTY)/src
+WHISPER_PREBUILT := $(WHISPER_THIRD_PARTY)/prebuilt/$(PLATFORM)
 
-build-libs: build-libs-llama
+.PHONY: build-libs build-libs-llama build-libs-whisper build-libs-linux build-libs-all clean verify
+
+build-libs: build-libs-llama build-libs-whisper
 
 # Build both native platform and linux-amd64 (via Docker)
 build-libs-all: build-libs build-libs-linux
@@ -46,13 +51,13 @@ $(LLAMA_PREBUILT): $(LLAMA_SRC)
 	@mkdir -p $(LLAMA_PREBUILT)
 	find $(LLAMA_SRC)/build -name "*.a" -exec cp {} $(LLAMA_PREBUILT)/ \;
 	@echo "==> Copying llama.cpp headers..."
-	@mkdir -p $(LLAMA_DIR)/include
-	cp $(LLAMA_SRC)/include/*.h $(LLAMA_DIR)/include/
-	@mkdir -p $(LLAMA_DIR)/ggml/include
-	cp $(LLAMA_SRC)/ggml/include/*.h $(LLAMA_DIR)/ggml/include/
-	@mkdir -p $(LLAMA_DIR)/common
-	cp $(LLAMA_SRC)/common/common.h $(LLAMA_DIR)/common/
-	cp $(LLAMA_SRC)/common/sampling.h $(LLAMA_DIR)/common/
+	@mkdir -p $(LLAMA_THIRD_PARTY)/include
+	cp $(LLAMA_SRC)/include/*.h $(LLAMA_THIRD_PARTY)/include/
+	@mkdir -p $(LLAMA_THIRD_PARTY)/ggml/include
+	cp $(LLAMA_SRC)/ggml/include/*.h $(LLAMA_THIRD_PARTY)/ggml/include/
+	@mkdir -p $(LLAMA_THIRD_PARTY)/common
+	cp $(LLAMA_SRC)/common/common.h $(LLAMA_THIRD_PARTY)/common/
+	cp $(LLAMA_SRC)/common/sampling.h $(LLAMA_THIRD_PARTY)/common/
 	@echo "==> llama.cpp $(LLAMA_VERSION) ready: $(LLAMA_PREBUILT)/"
 
 $(LLAMA_SRC):
@@ -62,20 +67,51 @@ $(LLAMA_SRC):
 	tar xzf llama.cpp.tar.gz --strip-components=1 -C $(LLAMA_SRC)
 	rm llama.cpp.tar.gz
 
+# ============================================================================
+# whisper.cpp
+# ============================================================================
+build-libs-whisper: $(WHISPER_PREBUILT)
+
+$(WHISPER_PREBUILT): $(WHISPER_SRC)
+	@echo "==> Building whisper.cpp $(WHISPER_VERSION) for $(PLATFORM)..."
+	cd $(WHISPER_SRC) && cmake -B build -DBUILD_SHARED_LIBS=OFF && \
+		cmake --build build --config Release -j$(NPROC)
+	@mkdir -p $(WHISPER_PREBUILT)
+	find $(WHISPER_SRC)/build -name "*.a" -exec cp {} $(WHISPER_PREBUILT)/ \;
+	@echo "==> Copying whisper.cpp headers..."
+	@mkdir -p $(WHISPER_THIRD_PARTY)/include
+	cp $(WHISPER_SRC)/include/*.h $(WHISPER_THIRD_PARTY)/include/
+	@mkdir -p $(WHISPER_THIRD_PARTY)/ggml/include
+	cp $(WHISPER_SRC)/ggml/include/*.h $(WHISPER_THIRD_PARTY)/ggml/include/
+	@echo "==> whisper.cpp $(WHISPER_VERSION) ready: $(WHISPER_PREBUILT)/"
+
+$(WHISPER_SRC):
+	@echo "==> Downloading whisper.cpp $(WHISPER_VERSION)..."
+	wget -qO whisper.cpp.tar.gz https://github.com/ggerganov/whisper.cpp/archive/refs/tags/$(WHISPER_VERSION).tar.gz
+	mkdir -p $(WHISPER_SRC)
+	tar xzf whisper.cpp.tar.gz --strip-components=1 -C $(WHISPER_SRC)
+	rm whisper.cpp.tar.gz
+
 # ============================================================================
 # Docker build for linux-amd64 (cross-compile from macOS)
 # ============================================================================
 build-libs-linux:
 	@echo "==> Building linux-amd64 static libraries via Docker..."
 	docker build -f Dockerfile.libs -o ./out .
-	@mkdir -p $(LLAMA_DIR)/prebuilt/linux-amd64
-	cp out/llama.cpp/linux-amd64/*.a $(LLAMA_DIR)/prebuilt/linux-amd64/
-	@# Copy headers if not already present
-	@mkdir -p $(LLAMA_DIR)/include $(LLAMA_DIR)/ggml/include $(LLAMA_DIR)/common
-	cp out/llama.cpp/include/*.h $(LLAMA_DIR)/include/
-	cp out/llama.cpp/ggml/include/*.h $(LLAMA_DIR)/ggml/include/
-	cp out/llama.cpp/common/common.h $(LLAMA_DIR)/common/
-	cp out/llama.cpp/common/sampling.h $(LLAMA_DIR)/common/
+	@# llama.cpp
+	@mkdir -p $(LLAMA_THIRD_PARTY)/prebuilt/linux-amd64
+	cp out/llama.cpp/linux-amd64/*.a $(LLAMA_THIRD_PARTY)/prebuilt/linux-amd64/
+	@mkdir -p $(LLAMA_THIRD_PARTY)/include $(LLAMA_THIRD_PARTY)/ggml/include $(LLAMA_THIRD_PARTY)/common
+	cp out/llama.cpp/include/*.h $(LLAMA_THIRD_PARTY)/include/
+	cp out/llama.cpp/ggml/include/*.h $(LLAMA_THIRD_PARTY)/ggml/include/
+	cp out/llama.cpp/common/common.h $(LLAMA_THIRD_PARTY)/common/
+	cp out/llama.cpp/common/sampling.h $(LLAMA_THIRD_PARTY)/common/
+	@# whisper.cpp
+	@mkdir -p $(WHISPER_THIRD_PARTY)/prebuilt/linux-amd64
+	cp out/whisper.cpp/linux-amd64/*.a $(WHISPER_THIRD_PARTY)/prebuilt/linux-amd64/
+	@mkdir -p $(WHISPER_THIRD_PARTY)/include $(WHISPER_THIRD_PARTY)/ggml/include
+	cp out/whisper.cpp/include/*.h $(WHISPER_THIRD_PARTY)/include/
+	cp out/whisper.cpp/ggml/include/*.h $(WHISPER_THIRD_PARTY)/ggml/include/
 	rm -rf out
 	@echo "==> linux-amd64 libraries ready"
 
@@ -83,16 +119,19 @@ build-libs-linux:
 # Verification
 # ============================================================================
 verify:
-	@echo "==> Verifying stub build (no tag)..."
+	@echo "==> Verifying stub builds (no tags)..."
 	go build ./ggml/llamacpp/...
-	@echo "==> Verifying CGO build (with tag)..."
+	go build ./ggml/whispercpp/...
+	@echo "==> Verifying CGO builds (with tags)..."
 	CGO_ENABLED=1 go build -tags llamacpp ./ggml/llamacpp/...
+	CGO_ENABLED=1 go build -tags whispercpp ./ggml/whispercpp/...
 	@echo "==> Running stub tests..."
 	go test ./ggml/llamacpp/...
+	go test ./ggml/whispercpp/...
 	@echo "==> All checks passed"
 
 # ============================================================================
 # Cleanup
 # ============================================================================
 clean:
-	rm -rf $(LLAMA_SRC) out
+	rm -rf $(LLAMA_SRC) $(WHISPER_SRC) out
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..23e2068
--- /dev/null
+++ b/README.md
@@ -0,0 +1,173 @@
+# go-nativeml
+
+Go bindings for C++ inference frameworks via CGO, with prebuilt static libraries for zero-dependency builds.
+
+## Supported Frameworks
+
+| Framework | Version | Package | Build Tag | Capabilities | Status |
+|-----------|---------|---------|-----------|--------------|--------|
+| [llama.cpp](https://github.com/ggerganov/llama.cpp) | `b8220` | `ggml/llamacpp` | `llamacpp` | Text generation, embeddings, tokenization | Available |
+| [whisper.cpp](https://github.com/ggerganov/whisper.cpp) | `v1.8.3` | `ggml/whispercpp` | `whispercpp` | Speech-to-text | Planned |
+
+## Quick Start
+
+```bash
+go get github.com/footprintai/go-nativeml
+```
+
+```go
+import "github.com/footprintai/go-nativeml/ggml/llamacpp"
+
+llamacpp.Init()
+defer llamacpp.Shutdown()
+
+model, _ := llamacpp.LoadModel("model.gguf", llamacpp.WithGPULayers(999))
+defer model.Close()
+
+ctx, _ := model.NewContext(llamacpp.WithContextSize(2048), llamacpp.WithThreads(4))
+defer ctx.Close()
+
+// Streaming generation
+ctx.GenerateStream("Hello, world", func(token string) bool {
+    fmt.Print(token)
+    return true // return false to stop early
+}, llamacpp.WithMaxTokens(256), llamacpp.WithTemperature(0.8))
+```
+
+## Build Tags
+
+| Tag | Behavior |
+|-----|----------|
+| _(none)_ | Stub implementations that return errors. Allows `go build` without CGO. |
+| `llamacpp` | Enables CGO bindings to prebuilt llama.cpp static libraries. |
+
+```bash
+# Stub build (no CGO required)
+go build ./...
+
+# CGO build with llama.cpp
+CGO_ENABLED=1 go build -tags llamacpp ./...
+```
+
+## API
+
+### Lifecycle
+
+```go
+llamacpp.Init()          // initialize backend
+llamacpp.Shutdown()      // cleanup
+```
+
+### Model
+
+```go
+model, err := llamacpp.LoadModel(path,
+    llamacpp.WithGPULayers(n),  // layers to offload to GPU
+)
+model.Close()
+model.EmbeddingSize()    // returns embedding dimension
+```
+
+### Context
+
+```go
+ctx, err := model.NewContext(
+    llamacpp.WithContextSize(2048),
+    llamacpp.WithThreads(4),
+    llamacpp.WithEmbeddings(),     // enable embedding mode
+)
+ctx.Close()
+```
+
+### Generation
+
+```go
+// Blocking
+text, err := ctx.Generate(prompt,
+    llamacpp.WithMaxTokens(256),
+    llamacpp.WithTemperature(0.8),
+    llamacpp.WithTopP(0.95),
+    llamacpp.WithTopK(40),
+    llamacpp.WithMinP(0.05),
+    llamacpp.WithRepeatPenalty(1.1),
+    llamacpp.WithSeed(42),
+)
+
+// Streaming
+err := ctx.GenerateStream(prompt, func(token string) bool {
+    fmt.Print(token)
+    return true // return false to cancel
+}, llamacpp.WithMaxTokens(256))
+```
+
+### Embeddings
+
+```go
+ctx, _ := model.NewContext(llamacpp.WithContextSize(512), llamacpp.WithEmbeddings())
+embeddings, err := ctx.GetEmbeddings("some text")  // []float32
+```
+
+### Tokenization
+
+```go
+tokens, err := ctx.Tokenize("some text")  // []int
+```
+
+## Examples
+
+```bash
+# Text generation
+CGO_ENABLED=1 go run -tags llamacpp ./examples/generate \
+    -model /path/to/model.gguf \
+    -prompt "Hello, world" \
+    -max-tokens 256 \
+    -temperature 0.8
+
+# Embeddings
+CGO_ENABLED=1 go run -tags llamacpp ./examples/embeddings \
+    -model /path/to/model.gguf \
+    -text "Hello, world"
+```
+
+## Supported Platforms
+
+| Platform | Status |
+|----------|--------|
+| darwin-amd64 (macOS Intel) | Prebuilt libraries included |
+| darwin-arm64 (macOS Apple Silicon) | Prebuilt libraries not yet available |
+| linux-amd64 | Prebuilt libraries included |
+
+## Building Libraries from Source
+
+For maintainers who need to rebuild the static libraries:
+
+```bash
+make build-libs          # Build for current platform
+make build-libs-linux    # Build linux-amd64 via Docker
+make build-libs-all      # Build native + linux-amd64
+make verify              # Run stub + CGO build checks
+make clean               # Remove temp build dirs
+```
+
+## Adding New Platforms
+
+1. Build llama.cpp static libraries for the target platform
+2. Place `.a` files in `third_party/llama.cpp/prebuilt/<os>-<arch>/`
+3. Add a `#cgo <os>,<arch> LDFLAGS` directive in `ggml/llamacpp/llamacpp.go`
+
+## Project Structure
+
+```
+ggml/llamacpp/         Go bindings for llama.cpp
+  llamacpp.go          CGO implementation (build tag: llamacpp)
+  llamacpp_stub.go     Stub implementation (default)
+  options.go           Option builders for model, context, generation
+  wrapper.h/.cpp       C++ bridge to llama.cpp APIs
+  bridge.c             CGO callback adapter
+third_party/llama.cpp/ Upstream headers + prebuilt static libraries
+examples/              Usage examples (generate, embeddings)
+```
+
+## License
+
+Apache-2.0
diff --git a/claude.md b/claude.md
index ae50c9a..0245a32 100644
--- a/claude.md
+++ b/claude.md
@@ -5,16 +5,19 @@ This project provides CGO wrappers for C++ inference frameworks for Go.
 ## Structure
 
 - `ggml/llamacpp/` — Go bindings for llama.cpp (build tag: `llamacpp`)
-- `ggml/whispercpp/` — (future) Go bindings for whisper.cpp
-- `third_party/llama.cpp/` — Upstream headers + prebuilt static libraries (keep upstream layout untouched)
+  - `third_party/` — Upstream headers + prebuilt static libraries
+- `ggml/whispercpp/` — Go bindings for whisper.cpp (build tag: `whispercpp`)
+  - `third_party/` — Upstream headers + prebuilt static libraries
+- `embed.go` files use `//go:embed` to ensure `go mod vendor` includes headers and `.a` files
 
 ## Build Tags
 
 - Default (no tag): stub implementations that return errors
 - `llamacpp`: enables CGO bindings to prebuilt llama.cpp libraries
+- `whispercpp`: enables CGO bindings to prebuilt whisper.cpp libraries
 
 ## Adding New Platforms
 
-1. Build llama.cpp static libraries for the target platform
-2. Place `.a` files in `third_party/llama.cpp/prebuilt/<os>-<arch>/`
-3. Add CGO LDFLAGS directive in `llamacpp.go`
+1. Build static libraries for the target platform
+2. Place `.a` files in `ggml/<pkg>/third_party/prebuilt/<os>-<arch>/`
+3. Add CGO LDFLAGS directive in the corresponding `.go` file
diff --git a/ggml/llamacpp/embed.go b/ggml/llamacpp/embed.go
new file mode 100644
index 0000000..91dae55
--- /dev/null
+++ b/ggml/llamacpp/embed.go
@@ -0,0 +1,16 @@
+// Copyright 2025 FootprintAI
+// SPDX-License-Identifier: Apache-2.0
+
+package llamacpp
+
+import "embed"
+
+// Embed directives ensure go mod vendor includes headers and prebuilt libraries.
+// The embedded filesystem is not used at runtime — CGO links directly via ${SRCDIR} paths.
+
+//go:embed third_party/include/*.h
+//go:embed third_party/ggml/include/*.h
+//go:embed third_party/common/*.h
+//go:embed third_party/prebuilt/darwin-amd64/*.a
+//go:embed third_party/prebuilt/linux-amd64/*.a
+var _ embed.FS
diff --git a/ggml/llamacpp/llamacpp.go b/ggml/llamacpp/llamacpp.go
index 6d75260..854e5f8 100644
--- a/ggml/llamacpp/llamacpp.go
+++ b/ggml/llamacpp/llamacpp.go
@@ -8,11 +8,11 @@
 package llamacpp
 
 /*
-#cgo CFLAGS: -I${SRCDIR}/../../third_party/llama.cpp/include -I${SRCDIR}/../../third_party/llama.cpp/ggml/include
-#cgo CXXFLAGS: -std=c++17 -I${SRCDIR}/../../third_party/llama.cpp/include -I${SRCDIR}/../../third_party/llama.cpp/ggml/include -I${SRCDIR}/../../third_party/llama.cpp/common
-#cgo darwin,arm64 LDFLAGS: -L${SRCDIR}/../../third_party/llama.cpp/prebuilt/darwin-arm64
-#cgo darwin,amd64 LDFLAGS: -L${SRCDIR}/../../third_party/llama.cpp/prebuilt/darwin-amd64
-#cgo linux,amd64 LDFLAGS: -L${SRCDIR}/../../third_party/llama.cpp/prebuilt/linux-amd64
+#cgo CFLAGS: -I${SRCDIR}/third_party/include -I${SRCDIR}/third_party/ggml/include
+#cgo CXXFLAGS: -std=c++17 -I${SRCDIR}/third_party/include -I${SRCDIR}/third_party/ggml/include -I${SRCDIR}/third_party/common
+#cgo darwin,arm64 LDFLAGS: -L${SRCDIR}/third_party/prebuilt/darwin-arm64
+#cgo darwin,amd64 LDFLAGS: -L${SRCDIR}/third_party/prebuilt/darwin-amd64
+#cgo linux,amd64 LDFLAGS: -L${SRCDIR}/third_party/prebuilt/linux-amd64
 #cgo LDFLAGS: -lcommon -lllama -lggml-cpu -lggml-base -lggml -lstdc++ -lm
 #cgo darwin LDFLAGS: -framework Accelerate -framework Metal -framework Foundation
 #include <stdlib.h>
diff --git a/third_party/llama.cpp/LICENSE b/ggml/llamacpp/third_party/LICENSE
similarity index 100%
rename from third_party/llama.cpp/LICENSE
rename to ggml/llamacpp/third_party/LICENSE
diff --git a/third_party/llama.cpp/common/common.h b/ggml/llamacpp/third_party/common/common.h
similarity index 100%
rename from third_party/llama.cpp/common/common.h
rename to ggml/llamacpp/third_party/common/common.h
diff --git a/third_party/llama.cpp/common/sampling.h b/ggml/llamacpp/third_party/common/sampling.h
similarity index 100%
rename from third_party/llama.cpp/common/sampling.h
rename to ggml/llamacpp/third_party/common/sampling.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-alloc.h b/ggml/llamacpp/third_party/ggml/include/ggml-alloc.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-alloc.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-alloc.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-backend.h b/ggml/llamacpp/third_party/ggml/include/ggml-backend.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-backend.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-backend.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-blas.h b/ggml/llamacpp/third_party/ggml/include/ggml-blas.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-blas.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-blas.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-cann.h b/ggml/llamacpp/third_party/ggml/include/ggml-cann.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-cann.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-cann.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-cpp.h b/ggml/llamacpp/third_party/ggml/include/ggml-cpp.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-cpp.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-cpp.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-cpu.h b/ggml/llamacpp/third_party/ggml/include/ggml-cpu.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-cpu.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-cpu.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-cuda.h b/ggml/llamacpp/third_party/ggml/include/ggml-cuda.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-cuda.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-cuda.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-hexagon.h b/ggml/llamacpp/third_party/ggml/include/ggml-hexagon.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-hexagon.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-hexagon.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-metal.h b/ggml/llamacpp/third_party/ggml/include/ggml-metal.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-metal.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-metal.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-opencl.h b/ggml/llamacpp/third_party/ggml/include/ggml-opencl.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-opencl.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-opencl.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-opt.h b/ggml/llamacpp/third_party/ggml/include/ggml-opt.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-opt.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-opt.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-rpc.h b/ggml/llamacpp/third_party/ggml/include/ggml-rpc.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-rpc.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-rpc.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-sycl.h b/ggml/llamacpp/third_party/ggml/include/ggml-sycl.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-sycl.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-sycl.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-virtgpu.h b/ggml/llamacpp/third_party/ggml/include/ggml-virtgpu.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-virtgpu.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-virtgpu.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-vulkan.h b/ggml/llamacpp/third_party/ggml/include/ggml-vulkan.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-vulkan.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-vulkan.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-webgpu.h b/ggml/llamacpp/third_party/ggml/include/ggml-webgpu.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-webgpu.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-webgpu.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-zdnn.h b/ggml/llamacpp/third_party/ggml/include/ggml-zdnn.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-zdnn.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-zdnn.h
diff --git a/third_party/llama.cpp/ggml/include/ggml-zendnn.h b/ggml/llamacpp/third_party/ggml/include/ggml-zendnn.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml-zendnn.h
rename to ggml/llamacpp/third_party/ggml/include/ggml-zendnn.h
diff --git a/third_party/llama.cpp/ggml/include/ggml.h b/ggml/llamacpp/third_party/ggml/include/ggml.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/ggml.h
rename to ggml/llamacpp/third_party/ggml/include/ggml.h
diff --git a/third_party/llama.cpp/ggml/include/gguf.h b/ggml/llamacpp/third_party/ggml/include/gguf.h
similarity index 100%
rename from third_party/llama.cpp/ggml/include/gguf.h
rename to ggml/llamacpp/third_party/ggml/include/gguf.h
diff --git a/third_party/llama.cpp/include/llama-cpp.h b/ggml/llamacpp/third_party/include/llama-cpp.h
similarity index 100%
rename from third_party/llama.cpp/include/llama-cpp.h
rename to ggml/llamacpp/third_party/include/llama-cpp.h
diff --git a/third_party/llama.cpp/include/llama.h b/ggml/llamacpp/third_party/include/llama.h
similarity index 100%
rename from third_party/llama.cpp/include/llama.h
rename to ggml/llamacpp/third_party/include/llama.h
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libcommon.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libcommon.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libcommon.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libcommon.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libcpp-httplib.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libcpp-httplib.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libcpp-httplib.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libcpp-httplib.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libggml-base.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-base.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libggml-base.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-base.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libggml-blas.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-blas.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libggml-blas.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-blas.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libggml-cpu.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-cpu.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libggml-cpu.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-cpu.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libggml-metal.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-metal.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libggml-metal.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml-metal.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libggml.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libggml.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libggml.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libgguf-model-data.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libgguf-model-data.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libgguf-model-data.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libgguf-model-data.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libllama.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libllama.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libllama.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libllama.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libmtmd.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libmtmd.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libmtmd.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libmtmd.a
diff --git a/third_party/llama.cpp/prebuilt/darwin-amd64/libserver-context.a b/ggml/llamacpp/third_party/prebuilt/darwin-amd64/libserver-context.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/darwin-amd64/libserver-context.a
rename to ggml/llamacpp/third_party/prebuilt/darwin-amd64/libserver-context.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libcommon.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libcommon.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libcommon.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libcommon.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libcpp-httplib.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libcpp-httplib.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libcpp-httplib.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libcpp-httplib.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libggml-base.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libggml-base.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libggml-base.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libggml-base.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libggml-cpu.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libggml-cpu.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libggml-cpu.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libggml-cpu.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libggml.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libggml.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libggml.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libggml.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libllama.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libllama.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libllama.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libllama.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libmtmd.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libmtmd.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libmtmd.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libmtmd.a
diff --git a/third_party/llama.cpp/prebuilt/linux-amd64/libserver-context.a b/ggml/llamacpp/third_party/prebuilt/linux-amd64/libserver-context.a
similarity index 100%
rename from third_party/llama.cpp/prebuilt/linux-amd64/libserver-context.a
rename to ggml/llamacpp/third_party/prebuilt/linux-amd64/libserver-context.a
diff --git a/ggml/whispercpp/embed.go b/ggml/whispercpp/embed.go
new file mode 100644
index 0000000..fb4fda8
--- /dev/null
+++ b/ggml/whispercpp/embed.go
@@ -0,0 +1,15 @@
+// Copyright 2025 FootprintAI
+// SPDX-License-Identifier: Apache-2.0
+
+package whispercpp
+
+import "embed"
+
+// Embed directives ensure go mod vendor includes headers and prebuilt libraries.
+// The embedded filesystem is not used at runtime — CGO links directly via ${SRCDIR} paths.
+
+//go:embed third_party/include/*.h
+//go:embed third_party/ggml/include/*.h
+//go:embed third_party/prebuilt/darwin-amd64/*.a
+//go:embed third_party/prebuilt/linux-amd64/*.a
+var _ embed.FS
diff --git a/ggml/whispercpp/options.go b/ggml/whispercpp/options.go
new file mode 100644
index 0000000..52798a2
--- /dev/null
+++ b/ggml/whispercpp/options.go
@@ -0,0 +1,105 @@
+//go:build whispercpp
+
+// Copyright 2025 FootprintAI
+// SPDX-License-Identifier: Apache-2.0
+
+package whispercpp
+
+// ModelOption configures model loading.
+type ModelOption func(*modelConfig)
+
+type modelConfig struct {
+	useGPU    bool
+	flashAttn bool
+}
+
+func defaultModelConfig() modelConfig {
+	return modelConfig{
+		useGPU:    true,
+		flashAttn: false,
+	}
+}
+
+// WithGPU enables or disables GPU acceleration.
+func WithGPU(enabled bool) ModelOption {
+	return func(c *modelConfig) { c.useGPU = enabled }
+}
+
+// WithFlashAttention enables or disables flash attention.
+func WithFlashAttention(enabled bool) ModelOption {
+	return func(c *modelConfig) { c.flashAttn = enabled }
+}
+
+// TranscribeOption configures transcription.
+type TranscribeOption func(*transcribeConfig)
+
+type transcribeConfig struct {
+	threads         int
+	language        string
+	translate       bool
+	timestamps      bool
+	tokenTimestamps bool
+	singleSegment   bool
+	temperature     float32
+	maxTokens       int
+	prompt          string
+}
+
+func defaultTranscribeConfig() transcribeConfig {
+	return transcribeConfig{
+		threads:         4,
+		language:        "auto",
+		translate:       false,
+		timestamps:      true,
+		tokenTimestamps: false,
+		singleSegment:   false,
+		temperature:     0.0,
+		maxTokens:       0,
+		prompt:          "",
+	}
+}
+
+// WithThreads sets the number of CPU threads for inference.
+func WithThreads(n int) TranscribeOption {
+	return func(c *transcribeConfig) { c.threads = n }
+}
+
+// WithLanguage sets the language for transcription (e.g. "en", "de", "auto").
+func WithLanguage(lang string) TranscribeOption {
+	return func(c *transcribeConfig) { c.language = lang }
+}
+
+// WithTranslate enables translation to English.
+func WithTranslate(enabled bool) TranscribeOption {
+	return func(c *transcribeConfig) { c.translate = enabled }
+}
+
+// WithTimestamps enables or disables timestamps in output.
+func WithTimestamps(enabled bool) TranscribeOption {
+	return func(c *transcribeConfig) { c.timestamps = enabled }
+}
+
+// WithTokenTimestamps enables token-level timestamps.
+func WithTokenTimestamps(enabled bool) TranscribeOption {
+	return func(c *transcribeConfig) { c.tokenTimestamps = enabled }
+}
+
+// WithSingleSegment forces output into a single segment.
+func WithSingleSegment(enabled bool) TranscribeOption {
+	return func(c *transcribeConfig) { c.singleSegment = enabled }
+}
+
+// WithTemperature sets the sampling temperature.
+func WithTemperature(t float32) TranscribeOption {
+	return func(c *transcribeConfig) { c.temperature = t }
+}
+
+// WithMaxTokens sets the maximum tokens per segment (0 = no limit).
+func WithMaxTokens(n int) TranscribeOption {
+	return func(c *transcribeConfig) { c.maxTokens = n }
+}
+
+// WithPrompt sets the initial prompt for the decoder.
+func WithPrompt(prompt string) TranscribeOption {
+	return func(c *transcribeConfig) { c.prompt = prompt }
+}
diff --git a/ggml/whispercpp/third_party/LICENSE b/ggml/whispercpp/third_party/LICENSE
new file mode 100644
index 0000000..acb96ce
--- /dev/null
+++ b/ggml/whispercpp/third_party/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023-2024 The ggml authors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-alloc.h b/ggml/whispercpp/third_party/ggml/include/ggml-alloc.h
new file mode 100644
index 0000000..78aa059
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-alloc.h
@@ -0,0 +1,85 @@
+#pragma once
+
+#include "ggml.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
+typedef struct      ggml_backend_buffer * ggml_backend_buffer_t;
+typedef struct             ggml_backend * ggml_backend_t;
+
+// Tensor allocator
+struct ggml_tallocr {
+    ggml_backend_buffer_t buffer;
+    void * base;
+    size_t alignment;
+    size_t offset;
+};
+
+GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
+GGML_API enum ggml_status    ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
+
+// Graph allocator
+/*
+  Example usage:
+    ggml_gallocr_t galloc = ggml_gallocr_new(ggml_backend_cpu_buffer_type());
+
+    // optional: create a worst-case graph and reserve the buffers to avoid reallocations
+    ggml_gallocr_reserve(galloc, build_graph(max_batch));
+
+    // allocate the graph
+    struct ggml_cgraph * graph = build_graph(batch);
+    ggml_gallocr_alloc_graph(galloc, graph);
+
+    printf("compute buffer size: %zu bytes\n", ggml_gallocr_get_buffer_size(galloc, 0));
+
+    // evaluate the graph
+    ggml_backend_graph_compute(backend, graph);
+*/
+
+// special tensor flags for use with the graph allocator:
+//   ggml_set_input(): all input tensors are allocated at the beginning of the graph in non-overlapping addresses
+//   ggml_set_output(): output tensors are never freed and never overwritten
+
+typedef struct ggml_gallocr * ggml_gallocr_t;
+
+GGML_API ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft);
+GGML_API ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs);
+GGML_API void           ggml_gallocr_free(ggml_gallocr_t galloc);
+
+// pre-allocate buffers from a measure graph - does not allocate or modify the graph
+// call with a worst-case graph to avoid buffer reallocations
+// not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
+// returns false if the buffer allocation failed
+// ggml_gallocr_resrve_n_size writes the buffer sizes per galloc buffer that would be allocated by ggml_gallocr_reserve_n to sizes
+GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
+GGML_API void ggml_gallocr_reserve_n_size(
+    ggml_gallocr_t galloc,
+    struct ggml_cgraph * graph,
+    const int * node_buffer_ids,
+    const int * leaf_buffer_ids,
+    size_t * sizes);
+GGML_API bool ggml_gallocr_reserve_n(
+    ggml_gallocr_t galloc,
+    struct ggml_cgraph * graph,
+    const int * node_buffer_ids,
+    const int * leaf_buffer_ids);
+
+// automatic reallocation if the topology changes when using a single buffer
+// returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)
+GGML_API bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
+
+GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id);
+
+// Utils
+// Create a buffer and allocate all the tensors in a ggml_context
+// ggml_backend_alloc_ctx_tensors_from_buft_size returns the size of the buffer that would be allocated by ggml_backend_alloc_ctx_tensors_from_buft
+GGML_API size_t                       ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
+GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
+GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-backend.h b/ggml/whispercpp/third_party/ggml/include/ggml-backend.h
new file mode 100644
index 0000000..a9d1778
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-backend.h
@@ -0,0 +1,373 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-alloc.h"
+
+#ifdef GGML_BACKEND_SHARED
+#    if defined(_WIN32) && !defined(__MINGW32__)
+#        ifdef GGML_BACKEND_BUILD
+#            define GGML_BACKEND_API __declspec(dllexport) extern
+#        else
+#            define GGML_BACKEND_API __declspec(dllimport) extern
+#        endif
+#    else
+#        define GGML_BACKEND_API __attribute__ ((visibility ("default"))) extern
+#    endif
+#else
+#    define GGML_BACKEND_API extern
+#endif
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+    typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
+    typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+    typedef struct ggml_backend_event * ggml_backend_event_t;
+    typedef struct ggml_backend * ggml_backend_t;
+    typedef void * ggml_backend_graph_plan_t;
+    typedef struct ggml_backend_reg * ggml_backend_reg_t;
+    typedef struct ggml_backend_device * ggml_backend_dev_t;
+
+
+    //
+    // Backend buffer type
+    //
+
+    GGML_API const char *          ggml_backend_buft_name          (ggml_backend_buffer_type_t buft);
+    GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer  (ggml_backend_buffer_type_t buft, size_t size);
+    GGML_API size_t                ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
+    GGML_API size_t                ggml_backend_buft_get_max_size  (ggml_backend_buffer_type_t buft);
+    GGML_API size_t                ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor);
+    GGML_API bool                  ggml_backend_buft_is_host       (ggml_backend_buffer_type_t buft);
+    GGML_API ggml_backend_dev_t    ggml_backend_buft_get_device    (ggml_backend_buffer_type_t buft);
+
+    //
+    // Backend buffer
+    //
+
+    enum ggml_backend_buffer_usage {
+        GGML_BACKEND_BUFFER_USAGE_ANY = 0,
+        GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
+        GGML_BACKEND_BUFFER_USAGE_COMPUTE = 2,
+    };
+
+    GGML_API const char *                   ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
+    GGML_API void                           ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
+    GGML_API void *                         ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
+    GGML_API size_t                         ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
+    GGML_API enum ggml_status               ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API size_t                         ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
+    GGML_API size_t                         ggml_backend_buffer_get_max_size  (ggml_backend_buffer_t buffer);
+    GGML_API size_t                         ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor);
+    GGML_API void                           ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
+    GGML_API bool                           ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
+    GGML_API void                           ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
+    GGML_API enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage     (ggml_backend_buffer_t buffer);
+    GGML_API ggml_backend_buffer_type_t     ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
+    GGML_API void                           ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
+
+    // tensor copy between different backends
+    GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    //
+    // Backend (stream)
+    //
+
+    GGML_API ggml_guid_t  ggml_backend_guid(ggml_backend_t backend);
+    GGML_API const char * ggml_backend_name(ggml_backend_t backend);
+    GGML_API void         ggml_backend_free(ggml_backend_t backend);
+
+    GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
+    GGML_API ggml_backend_buffer_t      ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
+    GGML_API size_t                     ggml_backend_get_alignment(ggml_backend_t backend);
+    GGML_API size_t                     ggml_backend_get_max_size(ggml_backend_t backend);
+
+    GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+
+    // "offset" refers to the offset in tensor->data for setting/getting data
+    GGML_API void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_memset(   struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);
+
+    GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
+
+    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API void                      ggml_backend_graph_plan_free  (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+
+    GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API enum ggml_status ggml_backend_graph_compute      (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+
+    // NOTE: will be removed, use device version instead
+    GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft);
+    GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
+
+    // asynchronous copy
+    // the copy is performed after all the currently queued operations in backend_src
+    // backend_dst will wait for the copy to complete before performing other operations
+    // automatic fallback to sync copy if async is not supported
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    GGML_API ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend);
+
+    //
+    // Events
+    //
+
+    GGML_API ggml_backend_event_t ggml_backend_event_new(ggml_backend_dev_t device);
+    GGML_API void                 ggml_backend_event_free(ggml_backend_event_t event);
+    GGML_API void                 ggml_backend_event_record(ggml_backend_event_t event, ggml_backend_t backend);
+    GGML_API void                 ggml_backend_event_synchronize(ggml_backend_event_t event);
+    GGML_API void                 ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event);
+
+    //
+    // Backend device
+    //
+
+    enum ggml_backend_dev_type {
+        // CPU device using system memory
+        GGML_BACKEND_DEVICE_TYPE_CPU,
+        // GPU device using dedicated memory
+        GGML_BACKEND_DEVICE_TYPE_GPU,
+        // integrated GPU device using host memory
+        GGML_BACKEND_DEVICE_TYPE_IGPU,
+        // accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
+        GGML_BACKEND_DEVICE_TYPE_ACCEL
+    };
+
+    // functionality supported by the device
+    struct ggml_backend_dev_caps {
+        // asynchronous operations
+        bool async;
+        // pinned host buffer
+        bool host_buffer;
+        // creating buffers from host ptr
+        bool buffer_from_host_ptr;
+        // event synchronization
+        bool events;
+    };
+
+    // all the device properties
+    struct ggml_backend_dev_props {
+        // device name
+        const char * name;
+        // device description
+        const char * description;
+        // device free memory in bytes
+        size_t memory_free;
+        // device total memory in bytes
+        size_t memory_total;
+        // device type
+        enum ggml_backend_dev_type type;
+        // device id
+        //   for PCI devices, this should be the PCI bus id formatted as "domain:bus:device.function" (e.g. "0000:01:00.0")
+        //   if the id is unknown, this should be NULL
+        const char * device_id;
+        // device capabilities
+        struct ggml_backend_dev_caps caps;
+    };
+
+    GGML_API const char *                  ggml_backend_dev_name(ggml_backend_dev_t device);
+    GGML_API const char *                  ggml_backend_dev_description(ggml_backend_dev_t device);
+    GGML_API void                          ggml_backend_dev_memory(ggml_backend_dev_t device, size_t * free, size_t * total);
+    GGML_API enum ggml_backend_dev_type    ggml_backend_dev_type(ggml_backend_dev_t device);
+    GGML_API void                          ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_dev_props * props);
+    GGML_API ggml_backend_reg_t            ggml_backend_dev_backend_reg(ggml_backend_dev_t device);
+    GGML_API ggml_backend_t                ggml_backend_dev_init(ggml_backend_dev_t device, const char * params);
+    GGML_API ggml_backend_buffer_type_t    ggml_backend_dev_buffer_type(ggml_backend_dev_t device);
+    GGML_API ggml_backend_buffer_type_t    ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device);
+    GGML_API ggml_backend_buffer_t         ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size);
+
+    GGML_API bool                          ggml_backend_dev_supports_op(ggml_backend_dev_t device, const struct ggml_tensor * op);
+    GGML_API bool                          ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buffer_type_t buft);
+    GGML_API bool                          ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op);
+
+    //
+    // Backend (reg)
+    //
+
+    GGML_API const char *       ggml_backend_reg_name(ggml_backend_reg_t reg);
+    GGML_API size_t             ggml_backend_reg_dev_count(ggml_backend_reg_t reg);
+    GGML_API ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index);
+    GGML_API void *             ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name);
+
+    // Common functions that may be obtained using ggml_backend_reg_get_proc_address
+
+    // Split buffer type for tensor parallelism
+    typedef ggml_backend_buffer_type_t   (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split);
+    // Set the number of threads for the backend
+    typedef void                         (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads);
+    // Get additional buffer types provided by the device (returns a NULL-terminated array)
+    typedef ggml_backend_buffer_type_t * (*ggml_backend_dev_get_extra_bufts_t)(ggml_backend_dev_t device);
+    // Set the abort callback for the backend
+    typedef void                         (*ggml_backend_set_abort_callback_t)(ggml_backend_t backend, ggml_abort_callback abort_callback, void * abort_callback_data);
+    // Get a list of feature flags supported by the backend (returns a NULL-terminated array)
+    struct ggml_backend_feature {
+        const char * name;
+        const char * value;
+    };
+    typedef struct ggml_backend_feature * (*ggml_backend_get_features_t)(ggml_backend_reg_t reg);
+
+    //
+    // Backend registry
+    //
+
+    GGML_API void ggml_backend_register(ggml_backend_reg_t reg);
+
+    GGML_API void ggml_backend_device_register(ggml_backend_dev_t device);
+
+    // Backend (reg) enumeration
+    GGML_API size_t             ggml_backend_reg_count(void);
+    GGML_API ggml_backend_reg_t ggml_backend_reg_get(size_t index);
+    GGML_API ggml_backend_reg_t ggml_backend_reg_by_name(const char * name);
+
+    // Device enumeration
+    GGML_API size_t             ggml_backend_dev_count(void);
+    GGML_API ggml_backend_dev_t ggml_backend_dev_get(size_t index);
+    GGML_API ggml_backend_dev_t ggml_backend_dev_by_name(const char * name);
+    GGML_API ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type);
+
+    // Direct backend (stream) initialization
+    // = ggml_backend_dev_init(ggml_backend_dev_by_name(name), params)
+    GGML_API ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params);
+    // = ggml_backend_dev_init(ggml_backend_dev_by_type(type), params)
+    GGML_API ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params);
+    // = ggml_backend_dev_init(ggml_backend_dev_by_type(GPU) OR ggml_backend_dev_by_type(CPU), NULL)
+    GGML_API ggml_backend_t ggml_backend_init_best(void);
+
+    // Load a backend from a dynamic library and register it
+    GGML_API ggml_backend_reg_t ggml_backend_load(const char * path);
+    // Unload a backend if loaded dynamically and unregister it
+    GGML_API void               ggml_backend_unload(ggml_backend_reg_t reg);
+    // Load all known backends from dynamic libraries
+    GGML_API void               ggml_backend_load_all(void);
+    GGML_API void               ggml_backend_load_all_from_path(const char * dir_path);
+
+    //
+    // Backend scheduler
+    //
+
+    // The backend scheduler allows for multiple backend devices to be used together
+    // Handles compute buffer allocation, assignment of tensors to backends, and copying of tensors between backends
+    // The backends are selected based on:
+    // - the backend that supports the operation
+    // - the location of the pre-allocated tensors (e.g. the weights)
+    /*
+      Example usage:
+
+        // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
+        // preferrably to run on the same backend as the buffer
+        ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+
+        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false, true);
+
+        // initialize buffers from a max size graph (optional)
+        reserve_graph = build_graph(sched, max_batch_size);
+
+        // manually assign nodes to a backend (optional, should not be needed in most cases)
+        struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
+        ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
+
+        ggml_backend_sched_reserve(sched, reserve_graph);
+
+        // compute
+        graph = build_graph(sched); // the graph and its tensors are single-use in terms of allocation, multi-use in terms of computation
+        for (int i = 0; i < 10; ++i) {
+            ggml_backend_sched_graph_compute(sched, graph); // on the first iteration the graph is allocated automatically
+        }
+
+        // if there are graph inputs:
+        graph = build_graph(sched); // get a new graph that is not allocated (the metadata for the old graph is freed once ggml_free is called)
+        ggml_backend_sched_reset(sched); // clear the allocation of the previous graph
+        ggml_backend_sched_alloc_graph(sched, graph); // explicitly allocate the new graph but do not execute it
+        ggml_backend_tensor_set(input_tensor, ...); // copy data to the newly allocated graph tensors
+        ggml_backend_sched_graph_compute(sched, graph); // execute the graph
+
+        // as an alternative to the above it is also possible to assign the inputs to a dedicated context and
+        // allocate them statically via ggml_backend_alloc_ctx_tensors
+    }
+    */
+
+    typedef struct ggml_backend_sched * ggml_backend_sched_t;
+
+    // Evaluation callback for each node in the graph (set with ggml_backend_sched_set_eval_callback)
+    // when ask == true, the scheduler wants to know if the user wants to observe this node
+    // this allows the scheduler to batch nodes together in order to evaluate them in a single call
+    //
+    // when ask == false, the scheduler is passing the node tensor to the user for observation
+    // if the user returns false, the scheduler will cancel the graph compute
+    //
+    typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
+
+    // Initialize a backend scheduler, backends with low index are given priority over backends with high index
+    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload);
+    GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
+
+    // Initialize backend buffers from a measure graph
+    GGML_API void                 ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes);
+    GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success
+
+    GGML_API int                  ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
+
+    // Get the number of splits of the last graph
+    GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
+    GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
+
+    GGML_API ggml_backend_buffer_type_t ggml_backend_sched_get_buffer_type(ggml_backend_sched_t sched, ggml_backend_t backend);
+    GGML_API size_t                     ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
+
+    GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
+
+    // Split graph without allocating it
+    GGML_API void                 ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+
+    // Allocate and compute graph on the backend scheduler
+    GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API void                 ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
+
+    // Reset all assignments and allocators - must be called before changing the node backends or allocating a new graph.
+    // This in effect deallocates all tensors that were previously allocated and leaves them with dangling pointers.
+    // The correct way to use this API is to discard the deallocated tensors and create new ones.
+    GGML_API void                 ggml_backend_sched_reset(ggml_backend_sched_t sched);
+
+    // Set a callback to be called for each resulting node during graph compute
+    GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
+
+    //
+    // Utils
+    //
+
+    struct ggml_backend_graph_copy {
+        ggml_backend_buffer_t buffer;
+        struct ggml_context * ctx_allocated;
+        struct ggml_context * ctx_unallocated;
+        struct ggml_cgraph * graph;
+    };
+
+    // Copy a graph to a different backend
+    GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
+    GGML_API void                           ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
+
+    typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
+
+    // Compare the output of two backends
+    GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data, struct ggml_tensor const * const * test_nodes, size_t num_test_nodes);
+
+    // Tensor initialization
+    GGML_API enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
+    GGML_API enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor);
+
+    // CPU buffer types are always available
+    GGML_API ggml_backend_buffer_t      ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
+    GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-blas.h b/ggml/whispercpp/third_party/ggml/include/ggml-blas.h
new file mode 100644
index 0000000..87a81b3
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-blas.h
@@ -0,0 +1,25 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_blas_init(void);
+
+GGML_BACKEND_API bool ggml_backend_is_blas(ggml_backend_t backend);
+
+// number of threads used for conversion to float
+// for openblas and blis, this will also set the number of threads used for blas operations
+GGML_BACKEND_API void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_blas_reg(void);
+
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-cann.h b/ggml/whispercpp/third_party/ggml/include/ggml-cann.h
new file mode 100644
index 0000000..b469e22
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-cann.h
@@ -0,0 +1,123 @@
+/*
+ * Copyright (c) 2023-2024 The ggml authors
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+ * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+ * IN THE SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Maximum number of CANN devices supported.
+ */
+#define GGML_CANN_MAX_DEVICES 16
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cann_reg(void);
+
+/**
+ * @brief Initializes the CANN backend for a specified device.
+ *
+ * This function initializes the CANN backend for the given device.
+ * It verifies the device index, allocates a context, and creates a backend
+ * instance.
+ *
+ * @param device The index of the device to initialize.
+ * @return A pointer to the initialized backend instance, or nullptr on failure.
+ */
+GGML_BACKEND_API ggml_backend_t ggml_backend_cann_init(int32_t device);
+
+/**
+ * @brief Checks if a given backend is a CANN backend.
+ *
+ * This function verifies if the provided backend is a CANN backend by comparing
+ * its GUID with the CANN backend's GUID.
+ *
+ * @param backend The backend instance to check.
+ * @return True if the backend is a CANN backend, false otherwise.
+ */
+GGML_BACKEND_API bool ggml_backend_is_cann(ggml_backend_t backend);
+
+/**
+ * @brief Retrieves the CANN buffer type for a specified device.
+ *
+ * This function initializes and returns the buffer type interface associated
+ * with the given device. It ensures thread-safe access using a mutex.
+ *
+ * @param device The device index for which to retrieve the buffer type.
+ * @return A pointer to the buffer type interface for the specified device, or
+ * nullptr if the device index is out of range.
+ */
+GGML_BACKEND_API ggml_backend_buffer_type_t
+ggml_backend_cann_buffer_type(int32_t device);
+
+/**
+ * @brief Retrieves the number of CANN devices available.
+ *
+ * This function returns the number of CANN devices available based on
+ * information obtained from `ggml_cann_info()`.
+ *
+ * @return The number of CANN devices available.
+ */
+GGML_BACKEND_API int32_t ggml_backend_cann_get_device_count(void);
+
+/**
+ * @brief pinned host buffer for use with the CPU backend for faster copies between CPU and NPU.
+ *
+ * @return A pointer to the host buffer type interface.
+ */
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type(void);
+
+/**
+ * @brief Retrieves the description of a specific CANN device.
+ *
+ * This function sets the specified device, retrieves the SoC name,
+ * and writes it into the provided description buffer.
+ *
+ * @param device The device index to retrieve the description for.
+ * @param description Pointer to a buffer where the description will be written.
+ * @param description_size Size of the description buffer.
+ */
+GGML_BACKEND_API void ggml_backend_cann_get_device_description(
+    int32_t device, char* description, size_t description_size);
+
+/**
+ * @brief Retrieves the memory information of a specific CANN device.
+ *
+ * This function sets the specified device, retrieves the free and total
+ * memory information of the specified type (ACL_HBM_MEM), and stores them
+ * in the provided pointers.
+ *
+ * @param device The device index to retrieve memory information for.
+ * @param free Pointer to a variable where the free memory size will be stored.
+ * @param total Pointer to a variable where the total memory size will be
+ * stored.
+ */
+GGML_BACKEND_API void ggml_backend_cann_get_device_memory(int32_t device,
+                                                  size_t* free,
+                                                  size_t* total);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-cpp.h b/ggml/whispercpp/third_party/ggml/include/ggml-cpp.h
new file mode 100644
index 0000000..48aa796
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-cpp.h
@@ -0,0 +1,39 @@
+#pragma once
+
+#ifndef __cplusplus
+#error "This header is for C++ only"
+#endif
+
+#include "ggml.h"
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#include "gguf.h"
+#include <memory>
+
+// Smart pointers for ggml types
+
+// ggml
+
+struct ggml_context_deleter { void operator()(ggml_context * ctx) { ggml_free(ctx); } };
+struct gguf_context_deleter { void operator()(gguf_context * ctx) { gguf_free(ctx); } };
+
+typedef std::unique_ptr<ggml_context, ggml_context_deleter> ggml_context_ptr;
+typedef std::unique_ptr<gguf_context, gguf_context_deleter> gguf_context_ptr;
+
+// ggml-alloc
+
+struct ggml_gallocr_deleter { void operator()(ggml_gallocr_t galloc) { ggml_gallocr_free(galloc); } };
+
+typedef std::unique_ptr<ggml_gallocr, ggml_gallocr_deleter> ggml_gallocr_ptr;
+
+// ggml-backend
+
+struct ggml_backend_deleter        { void operator()(ggml_backend_t backend)       { ggml_backend_free(backend); } };
+struct ggml_backend_buffer_deleter { void operator()(ggml_backend_buffer_t buffer) { ggml_backend_buffer_free(buffer); } };
+struct ggml_backend_event_deleter  { void operator()(ggml_backend_event_t event)   { ggml_backend_event_free(event); } };
+struct ggml_backend_sched_deleter  { void operator()(ggml_backend_sched_t sched)   { ggml_backend_sched_free(sched); } };
+
+typedef std::unique_ptr<ggml_backend,        ggml_backend_deleter>        ggml_backend_ptr;
+typedef std::unique_ptr<ggml_backend_buffer, ggml_backend_buffer_deleter> ggml_backend_buffer_ptr;
+typedef std::unique_ptr<ggml_backend_event,  ggml_backend_event_deleter>  ggml_backend_event_ptr;
+typedef std::unique_ptr<ggml_backend_sched,  ggml_backend_sched_deleter>  ggml_backend_sched_ptr;
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-cpu.h b/ggml/whispercpp/third_party/ggml/include/ggml-cpu.h
new file mode 100644
index 0000000..4f3b99c
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-cpu.h
@@ -0,0 +1,146 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+    // the compute plan that needs to be prepared for ggml_graph_compute()
+    // since https://github.com/ggml-org/ggml/issues/287
+    struct ggml_cplan {
+        size_t    work_size; // size of work buffer, calculated by `ggml_graph_plan()`
+        uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
+
+        int n_threads;
+        struct ggml_threadpool * threadpool;
+
+        // abort ggml_graph_compute when true
+        ggml_abort_callback abort_callback;
+        void *              abort_callback_data;
+    };
+
+    // numa strategies
+    enum ggml_numa_strategy {
+        GGML_NUMA_STRATEGY_DISABLED   = 0,
+        GGML_NUMA_STRATEGY_DISTRIBUTE = 1,
+        GGML_NUMA_STRATEGY_ISOLATE    = 2,
+        GGML_NUMA_STRATEGY_NUMACTL    = 3,
+        GGML_NUMA_STRATEGY_MIRROR     = 4,
+        GGML_NUMA_STRATEGY_COUNT
+    };
+
+    GGML_BACKEND_API void    ggml_numa_init(enum ggml_numa_strategy numa); // call once for better performance on NUMA systems
+    GGML_BACKEND_API bool    ggml_is_numa(void); // true if init detected that system has >1 NUMA node
+
+    GGML_BACKEND_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
+    GGML_BACKEND_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
+
+    GGML_BACKEND_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
+    GGML_BACKEND_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
+
+    GGML_BACKEND_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
+    GGML_BACKEND_API void    ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
+
+    GGML_BACKEND_API int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
+    GGML_BACKEND_API void    ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, int32_t value);
+
+    GGML_BACKEND_API float   ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
+    GGML_BACKEND_API void    ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
+
+    GGML_BACKEND_API float   ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3);
+    GGML_BACKEND_API void    ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, int i3, float value);
+
+    GGML_BACKEND_API struct ggml_threadpool *      ggml_threadpool_new           (struct ggml_threadpool_params  * params);
+    GGML_BACKEND_API void                          ggml_threadpool_free          (struct ggml_threadpool * threadpool);
+    GGML_BACKEND_API int                           ggml_threadpool_get_n_threads (struct ggml_threadpool * threadpool);
+    GGML_BACKEND_API void                          ggml_threadpool_pause         (struct ggml_threadpool * threadpool);
+    GGML_BACKEND_API void                          ggml_threadpool_resume        (struct ggml_threadpool * threadpool);
+
+    // ggml_graph_plan() has to be called before ggml_graph_compute()
+    // when plan.work_size > 0, caller must allocate memory for plan.work_data
+    GGML_BACKEND_API struct ggml_cplan ggml_graph_plan(
+                  const struct ggml_cgraph * cgraph,
+                                       int   n_threads, /* = GGML_DEFAULT_N_THREADS */
+                    struct ggml_threadpool * threadpool /* = NULL */ );
+    GGML_BACKEND_API enum ggml_status  ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
+
+    // same as ggml_graph_compute() but the work data is allocated as a part of the context
+    // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
+    GGML_BACKEND_API enum ggml_status  ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
+
+    //
+    // system info
+    //
+
+    // x86
+    GGML_BACKEND_API int ggml_cpu_has_sse3       (void);
+    GGML_BACKEND_API int ggml_cpu_has_ssse3      (void);
+    GGML_BACKEND_API int ggml_cpu_has_avx        (void);
+    GGML_BACKEND_API int ggml_cpu_has_avx_vnni   (void);
+    GGML_BACKEND_API int ggml_cpu_has_avx2       (void);
+    GGML_BACKEND_API int ggml_cpu_has_bmi2       (void);
+    GGML_BACKEND_API int ggml_cpu_has_f16c       (void);
+    GGML_BACKEND_API int ggml_cpu_has_fma        (void);
+    GGML_BACKEND_API int ggml_cpu_has_avx512     (void);
+    GGML_BACKEND_API int ggml_cpu_has_avx512_vbmi(void);
+    GGML_BACKEND_API int ggml_cpu_has_avx512_vnni(void);
+    GGML_BACKEND_API int ggml_cpu_has_avx512_bf16(void);
+    GGML_BACKEND_API int ggml_cpu_has_amx_int8   (void);
+    // ARM
+    GGML_BACKEND_API int ggml_cpu_has_neon       (void);
+    GGML_BACKEND_API int ggml_cpu_has_arm_fma    (void);
+    GGML_BACKEND_API int ggml_cpu_has_fp16_va    (void);
+    GGML_BACKEND_API int ggml_cpu_has_dotprod    (void);
+    GGML_BACKEND_API int ggml_cpu_has_matmul_int8(void);
+    GGML_BACKEND_API int ggml_cpu_has_sve        (void);
+    GGML_BACKEND_API int ggml_cpu_get_sve_cnt    (void);  // sve vector length in bytes
+    GGML_BACKEND_API int ggml_cpu_has_sme        (void);
+    // other
+    GGML_BACKEND_API int ggml_cpu_has_riscv_v    (void);
+    GGML_BACKEND_API int ggml_cpu_get_rvv_vlen   (void);  // risc-v vector length in bytes
+    GGML_BACKEND_API int ggml_cpu_has_vsx        (void);
+    GGML_BACKEND_API int ggml_cpu_has_vxe        (void);
+    GGML_BACKEND_API int ggml_cpu_has_wasm_simd  (void);
+    GGML_BACKEND_API int ggml_cpu_has_llamafile  (void);
+
+    // Internal types and functions exposed for tests and benchmarks
+
+    typedef void (*ggml_vec_dot_t)  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
+                                       const void * GGML_RESTRICT y, size_t by, int nrc);
+
+    struct ggml_type_traits_cpu {
+        ggml_from_float_t        from_float;
+        ggml_vec_dot_t           vec_dot;
+        enum ggml_type           vec_dot_type;
+        int64_t                  nrows; // number of rows to process simultaneously
+    };
+
+    GGML_BACKEND_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type);
+
+    GGML_BACKEND_API void ggml_cpu_init(void);
+
+    //
+    // CPU backend
+    //
+
+    GGML_BACKEND_API ggml_backend_t ggml_backend_cpu_init(void);
+
+    GGML_BACKEND_API bool ggml_backend_is_cpu                (ggml_backend_t backend);
+    GGML_BACKEND_API void ggml_backend_cpu_set_n_threads     (ggml_backend_t backend_cpu, int n_threads);
+    GGML_BACKEND_API void ggml_backend_cpu_set_threadpool    (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
+    GGML_BACKEND_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
+
+    GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
+
+    GGML_BACKEND_API void ggml_cpu_fp32_to_fp32(const float *,       float *, int64_t);
+    GGML_BACKEND_API void ggml_cpu_fp32_to_i32 (const float *,     int32_t *, int64_t);
+    GGML_BACKEND_API void ggml_cpu_fp32_to_fp16(const float *, ggml_fp16_t *, int64_t);
+    GGML_BACKEND_API void ggml_cpu_fp16_to_fp32(const ggml_fp16_t *, float *, int64_t);
+    GGML_BACKEND_API void ggml_cpu_fp32_to_bf16(const float *, ggml_bf16_t *, int64_t);
+    GGML_BACKEND_API void ggml_cpu_bf16_to_fp32(const ggml_bf16_t *, float *, int64_t);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-cuda.h b/ggml/whispercpp/third_party/ggml/include/ggml-cuda.h
new file mode 100644
index 0000000..22ad2c0
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-cuda.h
@@ -0,0 +1,47 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#ifdef GGML_USE_HIP
+#define GGML_CUDA_NAME "ROCm"
+#define GGML_CUBLAS_NAME "hipBLAS"
+#elif defined(GGML_USE_MUSA)
+#define GGML_CUDA_NAME "MUSA"
+#define GGML_CUBLAS_NAME "muBLAS"
+#else
+#define GGML_CUDA_NAME "CUDA"
+#define GGML_CUBLAS_NAME "cuBLAS"
+#endif
+#define GGML_CUDA_MAX_DEVICES       16
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_cuda_init(int device);
+
+GGML_BACKEND_API bool ggml_backend_is_cuda(ggml_backend_t backend);
+
+// device buffer
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
+
+// split tensor buffer that splits matrices by rows across multiple devices
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split);
+
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
+
+GGML_BACKEND_API int  ggml_backend_cuda_get_device_count(void);
+GGML_BACKEND_API void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
+GGML_BACKEND_API void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
+
+GGML_BACKEND_API bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
+GGML_BACKEND_API void ggml_backend_cuda_unregister_host_buffer(void * buffer);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cuda_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-hexagon.h b/ggml/whispercpp/third_party/ggml/include/ggml-hexagon.h
new file mode 100644
index 0000000..6e07900
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-hexagon.h
@@ -0,0 +1,19 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_hexagon_init(void);
+
+GGML_BACKEND_API bool ggml_backend_is_hexagon(ggml_backend_t backend);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_hexagon_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-metal.h b/ggml/whispercpp/third_party/ggml/include/ggml-metal.h
new file mode 100644
index 0000000..433838f
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-metal.h
@@ -0,0 +1,61 @@
+// Note: this description is outdated
+//
+// An interface allowing to compute ggml_cgraph with Metal
+//
+// This is a fully functional interface that extends ggml with GPU support for Apple devices.
+// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, etc.)
+//
+// How it works?
+//
+// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
+// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
+// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
+//
+// You only need to make sure that all memory buffers that you used during the graph creation
+// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
+// used during the graph evaluation to determine the arguments of the compute kernels.
+//
+// Synchronization between device and host memory (for example for input and output tensors)
+// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
+//
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#include <stddef.h>
+#include <stdbool.h>
+
+struct ggml_tensor;
+struct ggml_cgraph;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+//
+// backend API
+// user-code should use only these functions
+//
+
+// TODO: remove in the future
+GGML_BACKEND_API ggml_backend_t ggml_backend_metal_init(void);
+
+GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend);
+
+GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
+
+// helper to check if the device supports a specific family
+// ideally, the user code should be doing these checks
+// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
+GGML_BACKEND_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
+
+// capture all command buffers committed the next time `ggml_backend_graph_compute` is called
+GGML_BACKEND_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_metal_reg(void);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-opencl.h b/ggml/whispercpp/third_party/ggml/include/ggml-opencl.h
new file mode 100644
index 0000000..6b61771
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-opencl.h
@@ -0,0 +1,26 @@
+#ifndef GGML_OPENCL_H
+#define GGML_OPENCL_H
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+//
+// backend API
+//
+GGML_BACKEND_API ggml_backend_t ggml_backend_opencl_init(void);
+GGML_BACKEND_API bool ggml_backend_is_opencl(ggml_backend_t backend);
+
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_opencl_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
+
+#endif // GGML_OPENCL_H
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-opt.h b/ggml/whispercpp/third_party/ggml/include/ggml-opt.h
new file mode 100644
index 0000000..4703a05
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-opt.h
@@ -0,0 +1,256 @@
+// This file contains functionality for training models using GGML.
+// It is not strictly needed vs. just vanilla GGML but it provides a more high-level interface for common needs such as datasets.
+// At the bottom of this file especially there are relatively high-level functions that are suitable use or adaptation in user code.
+//
+// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de)
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#include <stdint.h>
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+    struct ggml_opt_dataset;
+    struct ggml_opt_context;
+    struct ggml_opt_result;
+
+    typedef struct ggml_opt_dataset * ggml_opt_dataset_t;
+    typedef struct ggml_opt_context * ggml_opt_context_t;
+    typedef struct ggml_opt_result  * ggml_opt_result_t;
+
+    // ====== Loss ======
+
+    // built-in loss types, i.e. the built-in quantities minimized by the optimizer
+    // custom loss types can be defined via mean or sum which simply reduce the outputs for all datapoints to a single value
+    enum ggml_opt_loss_type {
+        GGML_OPT_LOSS_TYPE_MEAN,
+        GGML_OPT_LOSS_TYPE_SUM,
+        GGML_OPT_LOSS_TYPE_CROSS_ENTROPY,
+        GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR,
+    };
+
+    // ====== Dataset ======
+
+    GGML_API ggml_opt_dataset_t ggml_opt_dataset_init(
+            enum ggml_type type_data,    // the type for the internal data tensor
+            enum ggml_type type_label,   // the type for the internal labels tensor
+            int64_t        ne_datapoint, // number of elements per datapoint
+            int64_t        ne_label,     // number of elements per label
+            int64_t        ndata,        // total number of datapoints/labels
+            int64_t        ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
+    GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset);
+
+    // get underlying tensors that store the data
+    GGML_API int64_t              ggml_opt_dataset_ndata (ggml_opt_dataset_t dataset);
+    GGML_API struct ggml_tensor * ggml_opt_dataset_data  (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata]
+    GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label,     ndata]
+
+    // shuffle idata first datapoints from dataset with RNG from opt_ctx, shuffle all datapoints if idata is negative
+    GGML_API void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata);
+
+    // get batch at position ibatch from dataset and copy the data to data_batch and labels_batch
+    GGML_API void ggml_opt_dataset_get_batch(
+            ggml_opt_dataset_t   dataset,
+            struct ggml_tensor * data_batch,   // shape = [ne_datapoint, ndata_batch]
+            struct ggml_tensor * labels_batch, // shape = [ne_label,     ndata_batch]
+            int64_t              ibatch);
+    GGML_API void ggml_opt_dataset_get_batch_host(
+            ggml_opt_dataset_t   dataset,
+            void               * data_batch,
+            size_t               nb_data_batch,
+            void               * labels_batch,
+            int64_t              ibatch);
+
+    // ====== Model / Context ======
+
+    enum ggml_opt_build_type {
+        GGML_OPT_BUILD_TYPE_FORWARD = 10,
+        GGML_OPT_BUILD_TYPE_GRAD    = 20,
+        GGML_OPT_BUILD_TYPE_OPT     = 30,
+    };
+
+    enum ggml_opt_optimizer_type {
+        GGML_OPT_OPTIMIZER_TYPE_ADAMW,
+        GGML_OPT_OPTIMIZER_TYPE_SGD,
+
+        GGML_OPT_OPTIMIZER_TYPE_COUNT
+    };
+
+    // parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
+    struct ggml_opt_optimizer_params {
+        struct {
+            float alpha; // learning rate
+            float beta1; // first AdamW momentum
+            float beta2; // second AdamW momentum
+            float eps;   // epsilon for numerical stability
+            float wd;    // weight decay - 0.0f to disable
+        } adamw;
+        struct {
+            float alpha; // learning rate
+            float wd;    // weight decay
+        } sgd;
+    };
+
+    // callback to calculate optimizer parameters prior to a backward pass
+    // userdata can be used to pass arbitrary data
+    typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata);
+
+    // returns the default optimizer params (constant, hard-coded values)
+    // userdata is not used
+    GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata);
+
+    // casts userdata to ggml_opt_optimizer_params and returns it
+    GGML_API struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata);
+
+    // parameters for initializing a new optimization context
+    struct ggml_opt_params {
+        ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs
+
+        // by default the forward graph needs to be reconstructed for each eval
+        // if ctx_compute, inputs, and outputs are set the graphs are instead allocated statically
+        struct ggml_context * ctx_compute;
+        struct ggml_tensor  * inputs;
+        struct ggml_tensor  * outputs;
+
+        enum ggml_opt_loss_type  loss_type;
+        enum ggml_opt_build_type build_type;
+
+        int32_t opt_period; // after how many gradient accumulation steps an optimizer step should be done
+
+        ggml_opt_get_optimizer_params get_opt_pars;    // callback for calculating optimizer parameters
+        void *                        get_opt_pars_ud; // userdata for calculating optimizer parameters
+
+        // only GGML_OPT_OPTIMIZER_TYPE_ADAMW needs m, v momenta per parameter tensor
+        enum ggml_opt_optimizer_type optimizer;
+    };
+
+    // get parameters for an optimization context with defaults set where possible
+    // parameters for which no sensible defaults exist are supplied as arguments to this function
+    GGML_API struct ggml_opt_params ggml_opt_default_params(
+            ggml_backend_sched_t    backend_sched,
+            enum ggml_opt_loss_type loss_type);
+
+    GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params);
+    GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx);
+
+    // set gradients to zero, initilize loss, and optionally reset the optimizer
+    GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer);
+
+    GGML_API bool ggml_opt_static_graphs(ggml_opt_context_t opt_ctx); // whether the graphs are allocated_statically
+
+    // get underlying tensors that store data
+    // if not using static graphs these pointers become invalid with the next call to ggml_opt_alloc
+    GGML_API struct ggml_tensor * ggml_opt_inputs(  ggml_opt_context_t opt_ctx); // forward graph input tensor
+    GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor
+    GGML_API struct ggml_tensor * ggml_opt_labels(  ggml_opt_context_t opt_ctx); // labels to compare outputs against
+    GGML_API struct ggml_tensor * ggml_opt_loss(    ggml_opt_context_t opt_ctx); // scalar tensor that contains the loss
+    GGML_API struct ggml_tensor * ggml_opt_pred(    ggml_opt_context_t opt_ctx); // predictions made by outputs
+    GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels
+
+    // get the gradient accumulator for a node from the forward graph
+    GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node);
+
+    GGML_API enum ggml_opt_optimizer_type ggml_opt_context_optimizer_type(ggml_opt_context_t); //TODO consistent naming scheme
+
+    GGML_API const char * ggml_opt_optimizer_name(enum ggml_opt_optimizer_type);
+
+    // ====== Optimization Result ======
+
+    GGML_API ggml_opt_result_t ggml_opt_result_init(void);
+    GGML_API void ggml_opt_result_free(ggml_opt_result_t result);
+    GGML_API void ggml_opt_result_reset(ggml_opt_result_t result);
+
+    // get data from result, uncertainties are optional and can be ignored by passing NULL
+    GGML_API void ggml_opt_result_ndata(   ggml_opt_result_t result, int64_t * ndata);                  // writes 1 value, number of datapoints
+    GGML_API void ggml_opt_result_loss(    ggml_opt_result_t result, double  * loss,     double * unc); // writes 1 value
+    GGML_API void ggml_opt_result_pred(    ggml_opt_result_t result, int32_t * pred);                   // writes ndata values
+    GGML_API void ggml_opt_result_accuracy(ggml_opt_result_t result, double  * accuracy, double * unc); // writes 1 value
+
+    // ====== Computation ======
+
+    // if not using static graphs, this function must be called prior to ggml_opt_alloc
+    GGML_API void ggml_opt_prepare_alloc(
+        ggml_opt_context_t    opt_ctx,
+        struct ggml_context * ctx_compute,
+        struct ggml_cgraph  * gf,
+        struct ggml_tensor  * inputs,
+        struct ggml_tensor  * outputs);
+
+    // allocate the next graph for evaluation, either forward or forward + backward
+    // must be called exactly once prior to calling ggml_opt_eval
+    GGML_API void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward);
+
+    // do forward pass, increment result if not NULL, do backward pass if allocated
+    GGML_API void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
+
+    // ############################################################################
+    // ## The high-level functions start here. They do not depend on any private ##
+    // ## functions or structs and can be copied to and adapted for user code.   ##
+    // ############################################################################
+
+    // ====== Intended Usage ======
+    //
+    // 1. Select the appropriate loss for your problem.
+    // 2. Create a dataset and set the data for the "data" tensor. Also set the "labels" tensor if your loss needs them.
+    //    Setting the shard size to 1 will be fine, it's the granularity with which data is shuffled/loaded (bigger values are faster).
+    // 3. Create a GGML graph for your model with no_alloc == true. Use two separate contexts for the tensors.
+    //    The first context should contain the model parameters and inputs and be allocated statically in user code.
+    //    The second context should contain all other tensors and will be (re)allocated automatically.
+    //    Due to this automated allocation the data of the second context is not defined when accessed in user code.
+    //    Note that the second dimension of the inputs/outputs are interpreted as the number of datapoints in those tensors.
+    // 4. Call ggml_opt_fit. If you need more control you can use ggml_opt_epoch instead.
+
+    // signature for a callback while evaluating opt_ctx on dataset, called after an evaluation
+    typedef void (*ggml_opt_epoch_callback)(
+            bool               train,       // true after training evaluation, false after validation evaluation
+            ggml_opt_context_t opt_ctx,
+            ggml_opt_dataset_t dataset,
+            ggml_opt_result_t  result,      // result associated with the dataset subsection
+            int64_t            ibatch,      // number of batches that have been evaluated so far
+            int64_t            ibatch_max,  // total number of batches in this dataset subsection
+            int64_t            t_start_us); // time at which the evaluation on the dataset subsection was started
+
+    // do training on front of dataset, do evaluation only on back of dataset
+    GGML_API void ggml_opt_epoch(
+            ggml_opt_context_t      opt_ctx,
+            ggml_opt_dataset_t      dataset,
+            ggml_opt_result_t       result_train,   // result to increment during training, ignored if NULL
+            ggml_opt_result_t       result_eval,    // result to increment during evaluation, ignored if NULL
+            int64_t                 idata_split,    // data index at which to split training and evaluation
+            ggml_opt_epoch_callback callback_train,
+            ggml_opt_epoch_callback callback_eval);
+
+    // callback that prints a progress bar on stderr
+    GGML_API void ggml_opt_epoch_callback_progress_bar(
+            bool               train,
+            ggml_opt_context_t opt_ctx,
+            ggml_opt_dataset_t dataset,
+            ggml_opt_result_t  result,
+            int64_t            ibatch,
+            int64_t            ibatch_max,
+            int64_t            t_start_us);
+
+    // fit model defined by inputs and outputs to dataset
+    GGML_API void ggml_opt_fit(
+            ggml_backend_sched_t            backend_sched,  // backend scheduler for constructing the compute graphs
+            struct ggml_context           * ctx_compute,    // context with temporarily allocated tensors to calculate the outputs
+            struct ggml_tensor            * inputs,         // input tensor with shape [ne_datapoint, ndata_batch]
+            struct ggml_tensor            * outputs,        // output tensor, must have shape [ne_label, ndata_batch] if labels are used
+            ggml_opt_dataset_t              dataset,        // dataset with data and optionally also labels
+            enum ggml_opt_loss_type         loss_type,      // loss to minimize
+            enum ggml_opt_optimizer_type    optimizer,      // sgd or adamw
+            ggml_opt_get_optimizer_params   get_opt_pars,   // callback to get optimizer params, userdata is pointer to epoch (of type int64_t)
+            int64_t                         nepoch,         // how many times the dataset should be iterated over
+            int64_t                         nbatch_logical, // datapoints optimizer step, must be a multiple of ndata_batch in inputs/outputs
+            float                           val_split,      // fraction of the dataset to use for validation, must be in [0.0f, 1.0f)
+            bool                            silent);        // whether or not info prints to stderr should be suppressed
+
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-rpc.h b/ggml/whispercpp/third_party/ggml/include/ggml-rpc.h
new file mode 100644
index 0000000..df1ad2a
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-rpc.h
@@ -0,0 +1,30 @@
+#pragma once
+
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define RPC_PROTO_MAJOR_VERSION    3
+#define RPC_PROTO_MINOR_VERSION    6
+#define RPC_PROTO_PATCH_VERSION    0
+#define GGML_RPC_MAX_SERVERS       16
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_rpc_init(const char * endpoint, uint32_t device);
+GGML_BACKEND_API bool ggml_backend_is_rpc(ggml_backend_t backend);
+
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint, uint32_t device);
+
+GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, uint32_t device, size_t * free, size_t * total);
+
+GGML_BACKEND_API void ggml_backend_rpc_start_server(const char * endpoint, const char * cache_dir,
+                                                    size_t n_threads, size_t n_devices, ggml_backend_dev_t * devices);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_reg(void);
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_add_server(const char * endpoint);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-sycl.h b/ggml/whispercpp/third_party/ggml/include/ggml-sycl.h
new file mode 100644
index 0000000..5ce349a
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-sycl.h
@@ -0,0 +1,49 @@
+//
+//  MIT license
+//  Copyright (C) 2024 Intel Corporation
+//  SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#define GGML_SYCL_NAME "SYCL"
+#define GGML_SYCL_MAX_DEVICES 48
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_sycl_init(int device);
+
+GGML_BACKEND_API bool ggml_backend_is_sycl(ggml_backend_t backend);
+
+// devide buffer
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);
+
+// split tensor buffer that splits matrices by rows across multiple devices
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
+
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
+
+GGML_BACKEND_API void ggml_backend_sycl_print_sycl_devices(void);
+GGML_BACKEND_API void ggml_backend_sycl_get_gpu_list(int *id_list, int max_len);
+GGML_BACKEND_API void ggml_backend_sycl_get_device_description(int device,
+                                                       char *description,
+                                                       size_t description_size);
+GGML_BACKEND_API int  ggml_backend_sycl_get_device_count();
+GGML_BACKEND_API void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
+
+// SYCL doesn't support registering host memory, keep here for reference
+// GGML_BACKEND_API bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
+// GGML_BACKEND_API void ggml_backend_sycl_unregister_host_buffer(void * buffer);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_sycl_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-vulkan.h b/ggml/whispercpp/third_party/ggml/include/ggml-vulkan.h
new file mode 100644
index 0000000..ed5ea5f
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-vulkan.h
@@ -0,0 +1,29 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_VK_NAME "Vulkan"
+#define GGML_VK_MAX_DEVICES 16
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_vk_init(size_t dev_num);
+
+GGML_BACKEND_API bool ggml_backend_is_vk(ggml_backend_t backend);
+GGML_BACKEND_API int  ggml_backend_vk_get_device_count(void);
+GGML_BACKEND_API void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
+GGML_BACKEND_API void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
+
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_vk_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-webgpu.h b/ggml/whispercpp/third_party/ggml/include/ggml-webgpu.h
new file mode 100644
index 0000000..65b8ed9
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-webgpu.h
@@ -0,0 +1,19 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_WEBGPU_NAME "WebGPU"
+
+// Needed for examples in ggml
+GGML_BACKEND_API ggml_backend_t ggml_backend_webgpu_init(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_webgpu_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-zdnn.h b/ggml/whispercpp/third_party/ggml/include/ggml-zdnn.h
new file mode 100644
index 0000000..fbf45b6
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-zdnn.h
@@ -0,0 +1,17 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// device buffer
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_type(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zdnn_reg(void);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml-zendnn.h b/ggml/whispercpp/third_party/ggml/include/ggml-zendnn.h
new file mode 100644
index 0000000..a30a3a9
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml-zendnn.h
@@ -0,0 +1,22 @@
+#pragma once
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_zendnn_init(void);
+
+GGML_BACKEND_API bool ggml_backend_is_zendnn(ggml_backend_t backend);
+
+// number of threads used for zendnn operations
+GGML_BACKEND_API void ggml_backend_zendnn_set_n_threads(ggml_backend_t backend_zendnn, int n_threads);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zendnn_reg(void);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/ggml.h b/ggml/whispercpp/third_party/ggml/include/ggml.h
new file mode 100644
index 0000000..b69583d
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/ggml.h
@@ -0,0 +1,2724 @@
+#pragma once
+
+//
+// GGML Tensor Library
+//
+// This documentation is still a work in progress.
+// If you wish some specific topics to be covered, feel free to drop a comment:
+//
+//   https://github.com/ggerganov/whisper.cpp/issues/40
+//
+// ## Overview
+//
+// This library implements:
+//
+//  - a set of tensor operations
+//  - automatic differentiation
+//  - basic optimization algorithms
+//
+// The aim of this library is to provide a minimalistic approach for various machine learning tasks. This includes,
+// but is not limited to, the following:
+//
+//  - linear regression
+//  - support vector machines
+//  - neural networks
+//
+// The library allows the user to define a certain function using the available tensor operations. This function
+// definition is represented internally via a computation graph. Each tensor operation in the function definition
+// corresponds to a node in the graph. Having the computation graph defined, the user can choose to compute the
+// function's value and/or its gradient with respect to the input variables. Optionally, the function can be optimized
+// using one of the available optimization algorithms.
+//
+// For example, here we define the function: f(x) = a*x^2 + b
+//
+//   {
+//       struct ggml_init_params params = {
+//           .mem_size   = 16*1024*1024,
+//           .mem_buffer = NULL,
+//       };
+//
+//       // memory allocation happens here
+//       struct ggml_context * ctx = ggml_init(params);
+//
+//       struct ggml_tensor * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+//
+//       ggml_set_param(ctx, x); // x is an input variable
+//
+//       struct ggml_tensor * a  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+//       struct ggml_tensor * b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+//       struct ggml_tensor * x2 = ggml_mul(ctx, x, x);
+//       struct ggml_tensor * f  = ggml_add(ctx, ggml_mul(ctx, a, x2), b);
+//
+//       ...
+//   }
+//
+// Notice that the function definition above does not involve any actual computation. The computation is performed only
+// when the user explicitly requests it. For example, to compute the function's value at x = 2.0:
+//
+//   {
+//       ...
+//
+//       struct ggml_cgraph * gf = ggml_new_graph(ctx);
+//       ggml_build_forward_expand(gf, f);
+//
+//       // set the input variable and parameter values
+//       ggml_set_f32(x, 2.0f);
+//       ggml_set_f32(a, 3.0f);
+//       ggml_set_f32(b, 4.0f);
+//
+//       ggml_graph_compute_with_ctx(ctx, &gf, n_threads);
+//
+//       printf("f = %f\n", ggml_get_f32_1d(f, 0));
+//
+//       ...
+//   }
+//
+// The actual computation is performed in the ggml_graph_compute() function.
+//
+// The ggml_new_tensor_...() functions create new tensors. They are allocated in the memory buffer provided to the
+// ggml_init() function. You have to be careful not to exceed the memory buffer size. Therefore, you have to know
+// in advance how much memory you need for your computation. Alternatively, you can allocate a large enough memory
+// and after defining the computation graph, call the ggml_used_mem() function to find out how much memory was
+// actually needed.
+//
+// The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic
+// differentiation and optimization algorithms.
+//
+// The described approach allows to define the function graph once and then compute its forward or backward graphs
+// multiple times. All computations will use the same memory buffer allocated in the ggml_init() function. This way
+// the user can avoid the memory allocation overhead at runtime.
+//
+// The library supports multi-dimensional tensors - up to 4 dimensions. The FP16 and FP32 data types are first class
+// citizens, but in theory the library can be extended to support FP8 and integer data types.
+//
+// Each tensor operation produces a new tensor. Initially the library was envisioned to support only the use of unary
+// and binary operations. Most of the available operations fall into one of these two categories. With time, it became
+// clear that the library needs to support more complex operations. The way to support these operations is not clear
+// yet, but a few examples are demonstrated in the following operations:
+//
+//   - ggml_permute()
+//   - ggml_conv_1d_1s()
+//   - ggml_conv_1d_2s()
+//
+// For each tensor operator, the library implements a forward and backward computation function. The forward function
+// computes the output tensor value given the input tensor values. The backward function computes the adjoint of the
+// input tensors given the adjoint of the output tensor. For a detailed explanation of what this means, take a
+// calculus class, or watch the following video:
+//
+//   What is Automatic Differentiation?
+//   https://www.youtube.com/watch?v=wG_nF1awSSY
+//
+//
+// ## Tensor data (struct ggml_tensor)
+//
+// The tensors are stored in memory via the ggml_tensor struct. The structure provides information about the size of
+// the tensor, the data type, and the memory buffer where the tensor data is stored. Additionally, it contains
+// pointers to the "source" tensors - i.e. the tensors that were used to compute the current tensor. For example:
+//
+//   {
+//       struct ggml_tensor * c = ggml_add(ctx, a, b);
+//
+//       assert(c->src[0] == a);
+//       assert(c->src[1] == b);
+//   }
+//
+// The multi-dimensional tensors are stored in row-major order. The ggml_tensor struct contains fields for the
+// number of elements in each dimension ("ne") as well as the number of bytes ("nb", a.k.a. stride). This allows
+// to store tensors that are not contiguous in memory, which is useful for operations such as transposition and
+// permutation. All tensor operations have to take the stride into account and not assume that the tensor is
+// contiguous in memory.
+//
+// The data of the tensor is accessed via the "data" pointer. For example:
+//
+//   {
+//       const int nx = 2;
+//       const int ny = 3;
+//
+//       struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
+//
+//       for (int y = 0; y < ny; y++) {
+//           for (int x = 0; x < nx; x++) {
+//               *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y;
+//           }
+//       }
+//
+//       ...
+//   }
+//
+// Alternatively, there are helper functions, such as ggml_get_f32_1d() and ggml_set_f32_1d() that can be used.
+//
+// ## The matrix multiplication operator (ggml_mul_mat)
+//
+// TODO
+//
+//
+// ## Multi-threading
+//
+// TODO
+//
+//
+// ## Overview of ggml.c
+//
+// TODO
+//
+//
+// ## SIMD optimizations
+//
+// TODO
+//
+//
+// ## Debugging ggml
+//
+// TODO
+//
+//
+
+#ifdef GGML_SHARED
+#    if defined(_WIN32) && !defined(__MINGW32__)
+#        ifdef GGML_BUILD
+#            define GGML_API __declspec(dllexport) extern
+#        else
+#            define GGML_API __declspec(dllimport) extern
+#        endif
+#    else
+#        define GGML_API __attribute__ ((visibility ("default"))) extern
+#    endif
+#else
+#    define GGML_API extern
+#endif
+
+// TODO: support for clang
+#ifdef __GNUC__
+#    define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
+#elif defined(_MSC_VER)
+#    define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define GGML_DEPRECATED(func, hint) func
+#endif
+
+#ifndef __GNUC__
+#    define GGML_ATTRIBUTE_FORMAT(...)
+#elif defined(__MINGW32__) && !defined(__clang__)
+#    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#    define GGML_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+
+#if defined(_WIN32) && !defined(_WIN32_WINNT)
+#    define _WIN32_WINNT 0x0A00
+#endif
+
+#include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+#include <stdio.h>
+
+#define GGML_FILE_MAGIC   0x67676d6c // "ggml"
+#define GGML_FILE_VERSION 2
+
+#define GGML_QNT_VERSION        2    // bump this on quantization format changes
+#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
+
+#define GGML_MAX_DIMS           4
+#define GGML_MAX_PARAMS         2048
+#define GGML_MAX_SRC            10
+#define GGML_MAX_N_THREADS      512
+#define GGML_MAX_OP_PARAMS      64
+
+#ifndef GGML_MAX_NAME
+#   define GGML_MAX_NAME        64
+#endif
+
+#define GGML_DEFAULT_N_THREADS  4
+#define GGML_DEFAULT_GRAPH_SIZE 2048
+
+#if UINTPTR_MAX == 0xFFFFFFFF
+    #define GGML_MEM_ALIGN 4
+#elif defined(__EMSCRIPTEN__)
+// emscripten uses max_align_t == 8, so we need GGML_MEM_ALIGN == 8 for 64-bit wasm.
+// (for 32-bit wasm, the first conditional is true and GGML_MEM_ALIGN stays 4.)
+// ref: https://github.com/ggml-org/llama.cpp/pull/18628
+    #define GGML_MEM_ALIGN 8
+#else
+    #define GGML_MEM_ALIGN 16
+#endif
+
+#define GGML_EXIT_SUCCESS 0
+#define GGML_EXIT_ABORTED 1
+
+// TODO: convert to enum https://github.com/ggml-org/llama.cpp/pull/16187#discussion_r2388538726
+#define GGML_ROPE_TYPE_NORMAL 0
+#define GGML_ROPE_TYPE_NEOX   2
+#define GGML_ROPE_TYPE_MROPE  8
+#define GGML_ROPE_TYPE_VISION 24
+#define GGML_ROPE_TYPE_IMROPE 40 // binary: 101000
+
+#define GGML_MROPE_SECTIONS   4
+
+#define GGML_UNUSED(x) (void)(x)
+#ifdef __CUDACC__
+template<typename... Args>
+__host__ __device__ constexpr inline void ggml_unused_vars_impl(Args&&...) noexcept {}
+#define GGML_UNUSED_VARS(...) ggml_unused_vars_impl(__VA_ARGS__)
+#else
+#define GGML_UNUSED_VARS(...) do { (void)sizeof((__VA_ARGS__, 0)); } while(0)
+#endif // __CUDACC__
+
+#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
+
+#ifndef NDEBUG
+#   define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0)
+#elif defined(__GNUC__)
+#   define GGML_UNREACHABLE() __builtin_unreachable()
+#elif defined(_MSC_VER)
+#   define GGML_UNREACHABLE() __assume(0)
+#else
+#   define GGML_UNREACHABLE() ((void) 0)
+#endif
+
+#ifdef __cplusplus
+#   define GGML_NORETURN [[noreturn]]
+#elif defined(_MSC_VER)
+#   define GGML_NORETURN __declspec(noreturn)
+#else
+#   define GGML_NORETURN _Noreturn
+#endif
+
+#define GGML_ABORT(...) ggml_abort(__FILE__, __LINE__, __VA_ARGS__)
+#define GGML_ASSERT(x) if (!(x)) GGML_ABORT("GGML_ASSERT(%s) failed", #x)
+
+// used to copy the number of elements and stride in bytes of tensors into local variables.
+// main purpose is to reduce code duplication and improve readability.
+//
+// example:
+//
+//    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+//    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
+//
+#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
+    const type prefix##0 = (pointer) ? (pointer)->array[0] : 0; \
+    GGML_UNUSED(prefix##0);
+#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_1    (type, prefix, pointer, array) \
+    const type prefix##1 = (pointer) ? (pointer)->array[1] : 0; \
+    GGML_UNUSED(prefix##1);
+#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_2    (type, prefix, pointer, array) \
+    const type prefix##2 = (pointer) ? (pointer)->array[2] : 0; \
+    GGML_UNUSED(prefix##2);
+#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_3  (type, prefix, pointer, array) \
+    const type prefix##3 = (pointer) ? (pointer)->array[3] : 0; \
+    GGML_UNUSED(prefix##3);
+
+#define GGML_TENSOR_UNARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
+#define GGML_TENSOR_BINARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
+#define GGML_TENSOR_TERNARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne2, src2, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb2, src2, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
+#define GGML_TENSOR_BINARY_OP_LOCALS01 \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb)
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+    // Function type used in fatal error callbacks
+    typedef void (*ggml_abort_callback_t)(const char * error_message);
+
+    // Set the abort callback (passing null will restore original abort functionality: printing a message to stdout)
+    // Returns the old callback for chaining
+    GGML_API ggml_abort_callback_t ggml_set_abort_callback(ggml_abort_callback_t callback);
+
+    GGML_NORETURN GGML_ATTRIBUTE_FORMAT(3, 4)
+    GGML_API void ggml_abort(const char * file, int line, const char * fmt, ...);
+
+    enum ggml_status {
+        GGML_STATUS_ALLOC_FAILED = -2,
+        GGML_STATUS_FAILED = -1,
+        GGML_STATUS_SUCCESS = 0,
+        GGML_STATUS_ABORTED = 1,
+    };
+
+    // get ggml_status name string
+    GGML_API const char * ggml_status_to_string(enum ggml_status status);
+
+    // ieee 754-2008 half-precision float16
+    // todo: make this not an integral type
+    typedef uint16_t ggml_fp16_t;
+    GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t);
+    GGML_API ggml_fp16_t ggml_fp32_to_fp16(float);
+    GGML_API void        ggml_fp16_to_fp32_row(const ggml_fp16_t *, float *, int64_t);
+    GGML_API void        ggml_fp32_to_fp16_row(const float *, ggml_fp16_t *, int64_t);
+
+    // google brain half-precision bfloat16
+    typedef struct { uint16_t bits; } ggml_bf16_t;
+    GGML_API ggml_bf16_t ggml_fp32_to_bf16(float);
+    GGML_API float       ggml_bf16_to_fp32(ggml_bf16_t);  // consider just doing << 16
+    GGML_API void        ggml_bf16_to_fp32_row(const ggml_bf16_t *, float *, int64_t);
+    GGML_API void        ggml_fp32_to_bf16_row_ref(const float *, ggml_bf16_t *, int64_t);
+    GGML_API void        ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t);
+
+    struct ggml_object;
+    struct ggml_context;
+    struct ggml_cgraph;
+
+    // NOTE: always add types at the end of the enum to keep backward compatibility
+    enum ggml_type {
+        GGML_TYPE_F32     = 0,
+        GGML_TYPE_F16     = 1,
+        GGML_TYPE_Q4_0    = 2,
+        GGML_TYPE_Q4_1    = 3,
+        // GGML_TYPE_Q4_2 = 4, support has been removed
+        // GGML_TYPE_Q4_3 = 5, support has been removed
+        GGML_TYPE_Q5_0    = 6,
+        GGML_TYPE_Q5_1    = 7,
+        GGML_TYPE_Q8_0    = 8,
+        GGML_TYPE_Q8_1    = 9,
+        GGML_TYPE_Q2_K    = 10,
+        GGML_TYPE_Q3_K    = 11,
+        GGML_TYPE_Q4_K    = 12,
+        GGML_TYPE_Q5_K    = 13,
+        GGML_TYPE_Q6_K    = 14,
+        GGML_TYPE_Q8_K    = 15,
+        GGML_TYPE_IQ2_XXS = 16,
+        GGML_TYPE_IQ2_XS  = 17,
+        GGML_TYPE_IQ3_XXS = 18,
+        GGML_TYPE_IQ1_S   = 19,
+        GGML_TYPE_IQ4_NL  = 20,
+        GGML_TYPE_IQ3_S   = 21,
+        GGML_TYPE_IQ2_S   = 22,
+        GGML_TYPE_IQ4_XS  = 23,
+        GGML_TYPE_I8      = 24,
+        GGML_TYPE_I16     = 25,
+        GGML_TYPE_I32     = 26,
+        GGML_TYPE_I64     = 27,
+        GGML_TYPE_F64     = 28,
+        GGML_TYPE_IQ1_M   = 29,
+        GGML_TYPE_BF16    = 30,
+        // GGML_TYPE_Q4_0_4_4 = 31, support has been removed from gguf files
+        // GGML_TYPE_Q4_0_4_8 = 32,
+        // GGML_TYPE_Q4_0_8_8 = 33,
+        GGML_TYPE_TQ1_0   = 34,
+        GGML_TYPE_TQ2_0   = 35,
+        // GGML_TYPE_IQ4_NL_4_4 = 36,
+        // GGML_TYPE_IQ4_NL_4_8 = 37,
+        // GGML_TYPE_IQ4_NL_8_8 = 38,
+        GGML_TYPE_MXFP4   = 39, // MXFP4 (1 block)
+        GGML_TYPE_COUNT   = 40,
+    };
+
+    // precision
+    enum ggml_prec {
+        GGML_PREC_DEFAULT =  0, // stored as ggml_tensor.op_params, 0 by default
+        GGML_PREC_F32     = 10,
+    };
+
+    // model file types
+    enum ggml_ftype {
+        GGML_FTYPE_UNKNOWN        = -1,
+        GGML_FTYPE_ALL_F32        = 0,
+        GGML_FTYPE_MOSTLY_F16     = 1,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0    = 2,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1    = 3,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
+        GGML_FTYPE_MOSTLY_Q8_0    = 7,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_0    = 8,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_1    = 9,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q2_K    = 10, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q3_K    = 11, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_K    = 12, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_K    = 13, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q6_K    = 14, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ2_XS  = 16, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ1_S   = 18, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ4_NL  = 19, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ3_S   = 20, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ2_S   = 21, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
+        GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
+        GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
+        GGML_FTYPE_MOSTLY_MXFP4   = 25, // except 1d tensors
+    };
+
+    // available tensor operations:
+    enum ggml_op {
+        GGML_OP_NONE = 0,
+
+        GGML_OP_DUP,
+        GGML_OP_ADD,
+        GGML_OP_ADD_ID,
+        GGML_OP_ADD1,
+        GGML_OP_ACC,
+        GGML_OP_SUB,
+        GGML_OP_MUL,
+        GGML_OP_DIV,
+        GGML_OP_SQR,
+        GGML_OP_SQRT,
+        GGML_OP_LOG,
+        GGML_OP_SIN,
+        GGML_OP_COS,
+        GGML_OP_SUM,
+        GGML_OP_SUM_ROWS,
+        GGML_OP_CUMSUM,
+        GGML_OP_MEAN,
+        GGML_OP_ARGMAX,
+        GGML_OP_COUNT_EQUAL,
+        GGML_OP_REPEAT,
+        GGML_OP_REPEAT_BACK,
+        GGML_OP_CONCAT,
+        GGML_OP_SILU_BACK,
+        GGML_OP_NORM, // normalize
+        GGML_OP_RMS_NORM,
+        GGML_OP_RMS_NORM_BACK,
+        GGML_OP_GROUP_NORM,
+        GGML_OP_L2_NORM,
+
+        GGML_OP_MUL_MAT,
+        GGML_OP_MUL_MAT_ID,
+        GGML_OP_OUT_PROD,
+
+        GGML_OP_SCALE,
+        GGML_OP_SET,
+        GGML_OP_CPY,
+        GGML_OP_CONT,
+        GGML_OP_RESHAPE,
+        GGML_OP_VIEW,
+        GGML_OP_PERMUTE,
+        GGML_OP_TRANSPOSE,
+        GGML_OP_GET_ROWS,
+        GGML_OP_GET_ROWS_BACK,
+        GGML_OP_SET_ROWS,
+        GGML_OP_DIAG,
+        GGML_OP_DIAG_MASK_INF,
+        GGML_OP_DIAG_MASK_ZERO,
+        GGML_OP_SOFT_MAX,
+        GGML_OP_SOFT_MAX_BACK,
+        GGML_OP_ROPE,
+        GGML_OP_ROPE_BACK,
+        GGML_OP_CLAMP,
+        GGML_OP_CONV_TRANSPOSE_1D,
+        GGML_OP_IM2COL,
+        GGML_OP_IM2COL_BACK,
+        GGML_OP_IM2COL_3D,
+        GGML_OP_CONV_2D,
+        GGML_OP_CONV_3D,
+        GGML_OP_CONV_2D_DW,
+        GGML_OP_CONV_TRANSPOSE_2D,
+        GGML_OP_POOL_1D,
+        GGML_OP_POOL_2D,
+        GGML_OP_POOL_2D_BACK,
+        GGML_OP_UPSCALE,
+        GGML_OP_PAD,
+        GGML_OP_PAD_REFLECT_1D,
+        GGML_OP_ROLL,
+        GGML_OP_ARANGE,
+        GGML_OP_TIMESTEP_EMBEDDING,
+        GGML_OP_ARGSORT,
+        GGML_OP_TOP_K,
+        GGML_OP_LEAKY_RELU,
+        GGML_OP_TRI,
+        GGML_OP_FILL,
+
+        GGML_OP_FLASH_ATTN_EXT,
+        GGML_OP_FLASH_ATTN_BACK,
+        GGML_OP_SSM_CONV,
+        GGML_OP_SSM_SCAN,
+        GGML_OP_WIN_PART,
+        GGML_OP_WIN_UNPART,
+        GGML_OP_GET_REL_POS,
+        GGML_OP_ADD_REL_POS,
+        GGML_OP_RWKV_WKV6,
+        GGML_OP_GATED_LINEAR_ATTN,
+        GGML_OP_RWKV_WKV7,
+        GGML_OP_SOLVE_TRI,
+
+        GGML_OP_UNARY,
+
+        GGML_OP_MAP_CUSTOM1,
+        GGML_OP_MAP_CUSTOM2,
+        GGML_OP_MAP_CUSTOM3,
+
+        GGML_OP_CUSTOM,
+
+        GGML_OP_CROSS_ENTROPY_LOSS,
+        GGML_OP_CROSS_ENTROPY_LOSS_BACK,
+        GGML_OP_OPT_STEP_ADAMW,
+        GGML_OP_OPT_STEP_SGD,
+
+        GGML_OP_GLU,
+
+        GGML_OP_COUNT,
+    };
+
+    enum ggml_unary_op {
+        GGML_UNARY_OP_ABS,
+        GGML_UNARY_OP_SGN,
+        GGML_UNARY_OP_NEG,
+        GGML_UNARY_OP_STEP,
+        GGML_UNARY_OP_TANH,
+        GGML_UNARY_OP_ELU,
+        GGML_UNARY_OP_RELU,
+        GGML_UNARY_OP_SIGMOID,
+        GGML_UNARY_OP_GELU,
+        GGML_UNARY_OP_GELU_QUICK,
+        GGML_UNARY_OP_SILU,
+        GGML_UNARY_OP_HARDSWISH,
+        GGML_UNARY_OP_HARDSIGMOID,
+        GGML_UNARY_OP_EXP,
+        GGML_UNARY_OP_EXPM1,
+        GGML_UNARY_OP_SOFTPLUS,
+        GGML_UNARY_OP_GELU_ERF,
+        GGML_UNARY_OP_XIELU,
+        GGML_UNARY_OP_FLOOR,
+        GGML_UNARY_OP_CEIL,
+        GGML_UNARY_OP_ROUND,
+        GGML_UNARY_OP_TRUNC,
+
+        GGML_UNARY_OP_COUNT,
+    };
+
+    enum ggml_glu_op {
+        GGML_GLU_OP_REGLU,
+        GGML_GLU_OP_GEGLU,
+        GGML_GLU_OP_SWIGLU,
+        GGML_GLU_OP_SWIGLU_OAI,
+        GGML_GLU_OP_GEGLU_ERF,
+        GGML_GLU_OP_GEGLU_QUICK,
+
+        GGML_GLU_OP_COUNT,
+    };
+
+    enum ggml_object_type {
+        GGML_OBJECT_TYPE_TENSOR,
+        GGML_OBJECT_TYPE_GRAPH,
+        GGML_OBJECT_TYPE_WORK_BUFFER
+    };
+
+    enum ggml_log_level {
+        GGML_LOG_LEVEL_NONE  = 0,
+        GGML_LOG_LEVEL_DEBUG = 1,
+        GGML_LOG_LEVEL_INFO  = 2,
+        GGML_LOG_LEVEL_WARN  = 3,
+        GGML_LOG_LEVEL_ERROR = 4,
+        GGML_LOG_LEVEL_CONT  = 5, // continue previous log
+    };
+
+    // this tensor...
+    enum ggml_tensor_flag {
+        GGML_TENSOR_FLAG_INPUT  =  1, // ...is an input for the GGML compute graph
+        GGML_TENSOR_FLAG_OUTPUT =  2, // ...is an output for the GGML compute graph
+        GGML_TENSOR_FLAG_PARAM  =  4, // ...contains trainable parameters
+        GGML_TENSOR_FLAG_LOSS   =  8, // ...defines loss for numerical optimization (multiple loss tensors add up)
+    };
+
+    enum ggml_tri_type {
+        GGML_TRI_TYPE_UPPER_DIAG = 0,
+        GGML_TRI_TYPE_UPPER      = 1,
+        GGML_TRI_TYPE_LOWER_DIAG = 2,
+        GGML_TRI_TYPE_LOWER      = 3
+    };
+
+    struct ggml_init_params {
+        // memory pool
+        size_t mem_size;   // bytes
+        void * mem_buffer; // if NULL, memory will be allocated internally
+        bool   no_alloc;   // don't allocate memory for the tensor data
+    };
+
+    // n-dimensional tensor
+    struct ggml_tensor {
+        enum ggml_type type;
+
+        struct ggml_backend_buffer * buffer;
+
+        int64_t ne[GGML_MAX_DIMS]; // number of elements
+        size_t  nb[GGML_MAX_DIMS]; // stride in bytes:
+                                   // nb[0] = ggml_type_size(type)
+                                   // nb[1] = nb[0]   * (ne[0] / ggml_blck_size(type)) + padding
+                                   // nb[i] = nb[i-1] * ne[i-1]
+
+        // compute data
+        enum ggml_op op;
+
+        // op params - allocated as int32_t for alignment
+        int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
+
+        int32_t flags;
+
+        struct ggml_tensor * src[GGML_MAX_SRC];
+
+        // source tensor and offset for views
+        struct ggml_tensor * view_src;
+        size_t               view_offs;
+
+        void * data;
+
+        char name[GGML_MAX_NAME];
+
+        void * extra; // extra things e.g. for ggml-cuda.cu
+
+        char padding[8];
+    };
+
+    static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
+
+    // Abort callback
+    // If not NULL, called before ggml computation
+    // If it returns true, the computation is aborted
+    typedef bool (*ggml_abort_callback)(void * data);
+
+
+    //
+    // GUID
+    //
+
+    // GUID types
+    typedef uint8_t ggml_guid[16];
+    typedef ggml_guid * ggml_guid_t;
+
+    GGML_API bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b);
+
+    // misc
+
+    GGML_API const char * ggml_version(void);
+    GGML_API const char * ggml_commit(void);
+
+    GGML_API void    ggml_time_init(void); // call this once at the beginning of the program
+    GGML_API int64_t ggml_time_ms(void);
+    GGML_API int64_t ggml_time_us(void);
+    GGML_API int64_t ggml_cycles(void);
+    GGML_API int64_t ggml_cycles_per_ms(void);
+
+    // accepts a UTF-8 path, even on Windows
+    GGML_API FILE *  ggml_fopen(const char * fname, const char * mode);
+
+    GGML_API void    ggml_print_object (const struct ggml_object * obj);
+    GGML_API void    ggml_print_objects(const struct ggml_context * ctx);
+
+    GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
+    GGML_API int64_t ggml_nrows     (const struct ggml_tensor * tensor);
+    GGML_API size_t  ggml_nbytes    (const struct ggml_tensor * tensor);
+    GGML_API size_t  ggml_nbytes_pad(const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
+
+    GGML_API int64_t ggml_blck_size(enum ggml_type type);
+    GGML_API size_t  ggml_type_size(enum ggml_type type);             // size in bytes for all elements in a block
+    GGML_API size_t  ggml_row_size (enum ggml_type type, int64_t ne); // size in bytes for all elements in a row
+
+    GGML_DEPRECATED(
+    GGML_API double ggml_type_sizef(enum ggml_type type), // ggml_type_size()/ggml_blck_size() as float
+    "use ggml_row_size() instead");
+
+    GGML_API const char * ggml_type_name(enum ggml_type type);
+    GGML_API const char * ggml_op_name  (enum ggml_op   op);
+    GGML_API const char * ggml_op_symbol(enum ggml_op   op);
+
+    GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
+    GGML_API const char * ggml_glu_op_name(enum ggml_glu_op op);
+    GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
+
+    GGML_API size_t  ggml_element_size(const struct ggml_tensor * tensor);
+
+    GGML_API bool    ggml_is_quantized(enum ggml_type type);
+
+    // TODO: temporary until model loading of ggml examples is refactored
+    GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
+
+    GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_permuted  (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_empty     (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_scalar    (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_vector    (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_matrix    (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_3d        (const struct ggml_tensor * tensor);
+    GGML_API int  ggml_n_dims       (const struct ggml_tensor * tensor); // returns 1 for scalars
+
+    // returns whether the tensor elements can be iterated over with a flattened index (no gaps, no permutation)
+    GGML_API bool ggml_is_contiguous  (const struct ggml_tensor * tensor);
+    GGML_API bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous()
+    GGML_API bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1
+    GGML_API bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2
+
+    // returns whether the tensor elements are allocated as one contiguous block of memory (no gaps, but permutation ok)
+    GGML_API bool ggml_is_contiguously_allocated(const struct ggml_tensor * tensor);
+
+    // true for tensor that is stored in memory as CxWxHxN and has been permuted to WxHxCxN
+    GGML_API bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor);
+
+    // true if the elements in dimension 0 are contiguous, or there is just 1 block of elements
+    GGML_API bool ggml_is_contiguous_rows(const struct ggml_tensor * tensor);
+
+    GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+    GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+
+    GGML_API bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
+
+    // use this to compute the memory overhead of a tensor
+    GGML_API size_t ggml_tensor_overhead(void);
+
+    GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);
+
+    // main
+
+    GGML_API struct ggml_context * ggml_init (struct ggml_init_params params);
+    GGML_API void                  ggml_reset(struct ggml_context * ctx);
+    GGML_API void                  ggml_free (struct ggml_context * ctx);
+
+    GGML_API size_t  ggml_used_mem(const struct ggml_context * ctx);
+
+    GGML_API bool    ggml_get_no_alloc(struct ggml_context * ctx);
+    GGML_API void    ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
+
+    GGML_API void *  ggml_get_mem_buffer     (const struct ggml_context * ctx);
+    GGML_API size_t  ggml_get_mem_size       (const struct ggml_context * ctx);
+    GGML_API size_t  ggml_get_max_tensor_size(const struct ggml_context * ctx);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int    n_dims,
+            const int64_t *ne);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_1d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_2d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0,
+            int64_t ne1);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_3d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0,
+            int64_t ne1,
+            int64_t ne2);
+
+    GGML_API struct ggml_tensor * ggml_new_tensor_4d(
+            struct ggml_context * ctx,
+            enum   ggml_type type,
+            int64_t ne0,
+            int64_t ne1,
+            int64_t ne2,
+            int64_t ne3);
+
+    GGML_API void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes);
+
+    GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
+    GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
+
+    // Context tensor enumeration and lookup
+    GGML_API struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx);
+    GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor);
+    GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
+
+    // Converts a flat index into coordinates
+    GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3);
+
+    GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
+    GGML_API enum ggml_glu_op ggml_get_glu_op(const struct ggml_tensor * tensor);
+
+    GGML_API void *  ggml_get_data    (const struct ggml_tensor * tensor);
+    GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
+
+    GGML_API const char *         ggml_get_name   (const struct ggml_tensor * tensor);
+    GGML_API struct ggml_tensor * ggml_set_name   (      struct ggml_tensor * tensor, const char * name);
+    GGML_ATTRIBUTE_FORMAT(2, 3)
+    GGML_API struct ggml_tensor * ggml_format_name(      struct ggml_tensor * tensor, const char * fmt, ...);
+
+    // Tensor flags
+    GGML_API void ggml_set_input(struct ggml_tensor * tensor);
+    GGML_API void ggml_set_output(struct ggml_tensor * tensor);
+    GGML_API void ggml_set_param(struct ggml_tensor * tensor);
+    GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
+
+    //
+    // operations on tensors with backpropagation
+    //
+
+    GGML_API struct ggml_tensor * ggml_dup(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_dup_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_add(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_add_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_add_cast(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            enum   ggml_type      type);
+
+    // dst[i0, i1, i2] = a[i0, i1, i2] + b[i0, ids[i1, i2]]
+    GGML_API struct ggml_tensor * ggml_add_id(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * ids);
+
+    GGML_API struct ggml_tensor * ggml_add1(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_add1_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // dst = a
+    // view(dst, nb1, nb2, nb3, offset) += b
+    // return dst
+    GGML_API struct ggml_tensor * ggml_acc(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_acc_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_sub(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_sub_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_mul(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_mul_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_div(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_div_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_sqr(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sqr_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sqrt(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sqrt_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_log(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_log_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_expm1(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_expm1_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_softplus(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_softplus_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sin(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sin_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_cos(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_cos_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // return scalar
+    GGML_API struct ggml_tensor * ggml_sum(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // sums along rows, with input shape [a,b,c,d] return shape [1,b,c,d]
+    GGML_API struct ggml_tensor * ggml_sum_rows(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_cumsum(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a);
+
+    // mean along rows
+    GGML_API struct ggml_tensor * ggml_mean(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // argmax along rows
+    GGML_API struct ggml_tensor * ggml_argmax(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // count number of equal elements in a and b
+    GGML_API struct ggml_tensor * ggml_count_equal(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // if a is the same shape as b, and a is not parameter, return a
+    // otherwise, return a new tensor: repeat(a) to fit in b
+    GGML_API struct ggml_tensor * ggml_repeat(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // repeat a to the specified shape
+    GGML_API struct ggml_tensor * ggml_repeat_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+                       int64_t    ne0,
+                       int64_t    ne1,
+                       int64_t    ne2,
+                       int64_t    ne3);
+
+    // sums repetitions in a into shape of b
+    GGML_API struct ggml_tensor * ggml_repeat_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b); // sum up values that are adjacent in dims > 0 instead of repeated with same stride
+
+    // concat a and b along dim
+    // used in stable-diffusion
+    GGML_API struct ggml_tensor * ggml_concat(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   dim);
+
+    GGML_API struct ggml_tensor * ggml_abs(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_abs_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sgn(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sgn_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_neg(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_neg_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_step(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_step_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_tanh(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_tanh_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_relu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_leaky_relu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a, float negative_slope, bool inplace);
+
+    GGML_API struct ggml_tensor * ggml_relu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sigmoid(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_sigmoid_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // GELU using erf (error function) when possible
+    // some backends may fallback to approximation based on Abramowitz and Stegun formula
+    GGML_API struct ggml_tensor * ggml_gelu_erf(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_erf_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_quick(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_gelu_quick_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_silu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_silu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // a - x
+    // b - dy
+    GGML_API struct ggml_tensor * ggml_silu_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // hardswish(x) = x * relu6(x + 3) / 6
+    GGML_API struct ggml_tensor * ggml_hardswish(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // hardsigmoid(x) = relu6(x + 3) / 6
+    GGML_API struct ggml_tensor * ggml_hardsigmoid(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_exp(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_exp_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_floor(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_floor_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_ceil(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_ceil_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_round(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_round_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+     /**
+     * Truncates the fractional part of each element in the tensor (towards zero).
+     * For example: trunc(3.7) = 3.0, trunc(-2.9) = -2.0
+     * Similar to std::trunc in C/C++.
+     */
+
+    GGML_API struct ggml_tensor * ggml_trunc(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_trunc_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+
+
+    // xIELU activation function
+    // x = x * (c_a(alpha_n) + c_b(alpha_p, beta) * sigmoid(beta * x)) + eps * (x > 0)
+    // where c_a = softplus and c_b(a, b) = softplus(a) + b are constraining functions
+    // that constrain the positive and negative source alpha values respectively
+    GGML_API struct ggml_tensor * ggml_xielu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float alpha_n,
+            float alpha_p,
+            float beta,
+            float eps);
+
+    // gated linear unit ops
+    // A: n columns, r rows,
+    // result is n / 2 columns, r rows,
+    // expects gate in second half of row, unless swapped is true
+    GGML_API struct ggml_tensor * ggml_glu(
+            struct ggml_context * ctx,
+             struct ggml_tensor * a,
+             enum ggml_glu_op     op,
+             bool                 swapped);
+
+    GGML_API struct ggml_tensor * ggml_reglu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_reglu_swapped(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_geglu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_geglu_swapped(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_swiglu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_swiglu_swapped(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_geglu_erf(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_geglu_erf_swapped(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_geglu_quick(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_geglu_quick_swapped(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // A: n columns, r rows,
+    // B: n columns, r rows,
+    GGML_API struct ggml_tensor * ggml_glu_split(
+            struct ggml_context * ctx,
+             struct ggml_tensor * a,
+             struct ggml_tensor * b,
+             enum ggml_glu_op     op);
+
+    GGML_API struct ggml_tensor * ggml_reglu_split(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_geglu_split(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_swiglu_split(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_geglu_erf_split(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_geglu_quick_split(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_swiglu_oai(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            float                 alpha,
+            float                 limit);
+
+    // normalize along rows
+    GGML_API struct ggml_tensor * ggml_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_rms_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    // group normalize along ne0*ne1*n_groups
+    // used in stable-diffusion
+    GGML_API struct ggml_tensor * ggml_group_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_groups,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_group_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_groups,
+            float                 eps);
+
+    // l2 normalize along rows
+    // used in rwkv v7
+    GGML_API struct ggml_tensor * ggml_l2_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    GGML_API struct ggml_tensor * ggml_l2_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 eps);
+
+    // a - x
+    // b - dy
+    GGML_API struct ggml_tensor * ggml_rms_norm_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            float                 eps);
+
+    // A: k columns, n rows => [ne03, ne02, n, k]
+    // B: k columns, m rows  (i.e. we transpose it internally) => [ne03 * x, ne02 * y, m, k]
+    // result is n columns, m rows => [ne03 * x, ne02 * y, m, n]
+    GGML_API struct ggml_tensor * ggml_mul_mat(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // change the precision of a matrix multiplication
+    // set to GGML_PREC_F32 for higher precision (useful for phi-2)
+    GGML_API void ggml_mul_mat_set_prec(
+            struct ggml_tensor * a,
+            enum ggml_prec       prec);
+
+    // indirect matrix multiplication
+    GGML_API struct ggml_tensor * ggml_mul_mat_id(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * as,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * ids);
+
+    // A: m columns, n rows,
+    // B: p columns, n rows,
+    // result is m columns, p rows
+    GGML_API struct ggml_tensor * ggml_out_prod(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    //
+    // operations on tensors without backpropagation
+    //
+
+    GGML_API struct ggml_tensor * ggml_scale(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 s);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_scale_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 s);
+
+    // x = s * a + b
+    GGML_API struct ggml_tensor * ggml_scale_bias(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float                 s,
+        float                 b);
+
+    GGML_API struct ggml_tensor * ggml_scale_bias_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        float                 s,
+        float                 b);
+
+    // b -> view(a,offset,nb1,nb2,3), return modified a
+    GGML_API struct ggml_tensor * ggml_set(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset); // in bytes
+
+    // b -> view(a,offset,nb1,nb2,3), return view(a)
+    GGML_API struct ggml_tensor * ggml_set_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                nb2,
+            size_t                nb3,
+            size_t                offset); // in bytes
+
+    GGML_API struct ggml_tensor * ggml_set_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                offset); // in bytes
+
+    GGML_API struct ggml_tensor * ggml_set_1d_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                offset); // in bytes
+
+    // b -> view(a,offset,nb1,nb2,3), return modified a
+    GGML_API struct ggml_tensor * ggml_set_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                offset); // in bytes
+
+    // b -> view(a,offset,nb1,nb2,3), return view(a)
+    GGML_API struct ggml_tensor * ggml_set_2d_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            size_t                nb1,
+            size_t                offset); // in bytes
+
+    // a -> b, return view(b)
+    GGML_API struct ggml_tensor * ggml_cpy(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // note: casting from f32 to i32 will discard the fractional part
+    GGML_API struct ggml_tensor * ggml_cast(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum   ggml_type      type);
+
+    // make contiguous
+    GGML_API struct ggml_tensor * ggml_cont(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // make contiguous, with new shape
+    GGML_API struct ggml_tensor * ggml_cont_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0);
+
+    GGML_API struct ggml_tensor * ggml_cont_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1);
+
+    GGML_API struct ggml_tensor * ggml_cont_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2);
+
+    GGML_API struct ggml_tensor * ggml_cont_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3);
+
+    // return view(a), b specifies the new shape
+    // TODO: when we start computing gradient, make a copy instead of view
+    GGML_API struct ggml_tensor * ggml_reshape(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // return view(a)
+    // TODO: when we start computing gradient, make a copy instead of view
+    GGML_API struct ggml_tensor * ggml_reshape_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0);
+
+    GGML_API struct ggml_tensor * ggml_reshape_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1);
+
+    // return view(a)
+    // TODO: when we start computing gradient, make a copy instead of view
+    GGML_API struct ggml_tensor * ggml_reshape_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2);
+
+    GGML_API struct ggml_tensor * ggml_reshape_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3);
+
+    // offset in bytes
+    GGML_API struct ggml_tensor * ggml_view_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_view_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            size_t                nb1, // row stride in bytes
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_view_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            size_t                nb1, // row   stride in bytes
+            size_t                nb2, // slice stride in bytes
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_view_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3,
+            size_t                nb1, // row   stride in bytes
+            size_t                nb2, // slice stride in bytes
+            size_t                nb3,
+            size_t                offset);
+
+    GGML_API struct ggml_tensor * ggml_permute(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   axis0,
+            int                   axis1,
+            int                   axis2,
+            int                   axis3);
+
+    // alias for ggml_permute(ctx, a, 1, 0, 2, 3)
+    GGML_API struct ggml_tensor * ggml_transpose(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // supports 4D a:
+    // a     [n_embd, ne1, ne2, ne3]
+    // b I32 [n_rows, ne2, ne3, 1]
+    //
+    // return [n_embd, n_rows, ne2, ne3]
+    GGML_API struct ggml_tensor * ggml_get_rows(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // data
+            struct ggml_tensor  * b); // row indices
+
+    GGML_API struct ggml_tensor * ggml_get_rows_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // gradients of ggml_get_rows result
+            struct ggml_tensor  * b,  // row indices
+            struct ggml_tensor  * c); // data for ggml_get_rows, only used for its shape
+
+    // a TD  [n_embd, ne1,    ne2,    ne3]
+    // b TS  [n_embd, n_rows, ne02,   ne03] | ne02 == ne2, ne03 == ne3
+    // c I64 [n_rows, ne11,   ne12,   1]    | c[i] in [0, ne1)
+    //
+    // undefined behavior if destination rows overlap
+    //
+    // broadcast:
+    //   ne2 % ne11 == 0
+    //   ne3 % ne12 == 0
+    //
+    // return view(a)
+    GGML_API struct ggml_tensor * ggml_set_rows(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // destination
+            struct ggml_tensor  * b,  // source
+            struct ggml_tensor  * c); // row indices
+
+    GGML_API struct ggml_tensor * ggml_diag(
+        struct ggml_context     * ctx,
+        struct ggml_tensor      * a);
+
+    // set elements above the diagonal to -INF
+    GGML_API struct ggml_tensor * ggml_diag_mask_inf(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_diag_mask_inf_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    // set elements above the diagonal to 0
+    GGML_API struct ggml_tensor * ggml_diag_mask_zero(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past);
+
+    GGML_API struct ggml_tensor * ggml_soft_max(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_soft_max_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    // a    [ne0, ne01, ne02, ne03]
+    // mask [ne0, ne11, ne12, ne13] | ne11 >= ne01, F16 or F32, optional
+    //
+    // broadcast:
+    //   ne02 % ne12 == 0
+    //   ne03 % ne13 == 0
+    //
+    // fused soft_max(a*scale + mask*(ALiBi slope))
+    // max_bias = 0.0f for no ALiBi
+    GGML_API struct ggml_tensor * ggml_soft_max_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * mask,
+            float                 scale,
+            float                 max_bias);
+
+    GGML_API struct ggml_tensor * ggml_soft_max_ext_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * mask,
+            float                 scale,
+            float                 max_bias);
+
+    GGML_API void ggml_soft_max_add_sinks(
+            struct ggml_tensor * a,
+            struct ggml_tensor * sinks);
+
+    GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            float                 scale,
+            float                 max_bias);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_soft_max_ext_back_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            float                 scale,
+            float                 max_bias);
+
+    // rotary position embedding
+    // if (mode & 1) - skip n_past elements (NOT SUPPORTED)
+    // if (mode & GGML_ROPE_TYPE_NEOX) - GPT-NeoX style
+    //
+    // b is an int32 vector with size a->ne[2], it contains the positions
+    GGML_API struct ggml_tensor * ggml_rope(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode);
+
+    // custom RoPE
+    // c is freq factors (e.g. phi3-128k), (optional)
+    GGML_API struct ggml_tensor * ggml_rope_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+    GGML_API struct ggml_tensor * ggml_rope_multi(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[GGML_MROPE_SECTIONS],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_ext_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+    GGML_API struct ggml_tensor * ggml_rope_multi_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[GGML_MROPE_SECTIONS],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow),
+        "use ggml_rope_ext instead");
+
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow),
+        "use ggml_rope_ext_inplace instead");
+
+    // compute correction dims for YaRN RoPE scaling
+    GGML_API void ggml_rope_yarn_corr_dims(
+        int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]);
+
+    // rotary position embedding backward, i.e compute dx from dy
+    // a - dy
+    GGML_API struct ggml_tensor * ggml_rope_ext_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a, // gradients of ggml_rope result
+            struct ggml_tensor  * b, // positions
+            struct ggml_tensor  * c, // freq factors
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+    GGML_API struct ggml_tensor * ggml_rope_multi_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[4],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
+
+    // clamp
+    // in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_clamp(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 min,
+            float                 max);
+
+    // im2col
+    // converts data into a format that effectively results in a convolution when combined with matrix multiplication
+    GGML_API struct ggml_tensor * ggml_im2col(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // convolution kernel
+            struct ggml_tensor  * b,  // data
+            int                   s0, // stride dimension 0
+            int                   s1, // stride dimension 1
+            int                   p0, // padding dimension 0
+            int                   p1, // padding dimension 1
+            int                   d0, // dilation dimension 0
+            int                   d1, // dilation dimension 1
+            bool                  is_2D,
+            enum ggml_type        dst_type);
+
+    GGML_API struct ggml_tensor * ggml_im2col_back(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,  // convolution kernel
+        struct ggml_tensor  * b,  // gradient of im2col output
+        int64_t             * ne, // shape of im2col input
+        int                   s0, // stride dimension 0
+        int                   s1, // stride dimension 1
+        int                   p0, // padding dimension 0
+        int                   p1, // padding dimension 1
+        int                   d0, // dilation dimension 0
+        int                   d1, // dilation dimension 1
+        bool                  is_2D);
+
+    GGML_API struct ggml_tensor * ggml_conv_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
+
+    // conv_1d with padding = half
+    // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
+    GGML_API struct ggml_tensor* ggml_conv_1d_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // convolution kernel
+            struct ggml_tensor  * b,  // data
+            int                   s,  // stride
+            int                   d); // dilation
+
+    // depthwise
+    // TODO: this is very likely wrong for some cases! - needs more testing
+    GGML_API struct ggml_tensor * ggml_conv_1d_dw(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
+
+    GGML_API struct ggml_tensor * ggml_conv_1d_dw_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   d0); // dilation
+
+    GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
+
+    GGML_API struct ggml_tensor * ggml_conv_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel
+            struct ggml_tensor  * b,   // data
+            int                   s0,  // stride dimension 0
+            int                   s1,  // stride dimension 1
+            int                   p0,  // padding dimension 0
+            int                   p1,  // padding dimension 1
+            int                   d0,  // dilation dimension 0
+            int                   d1); // dilation dimension 1
+
+    GGML_API struct ggml_tensor * ggml_im2col_3d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int64_t               IC,
+            int                   s0, // stride width
+            int                   s1, // stride height
+            int                   s2, // stride depth
+            int                   p0, // padding width
+            int                   p1, // padding height
+            int                   p2, // padding depth
+            int                   d0, // dilation width
+            int                   d1, // dilation height
+            int                   d2, // dilation depth
+            enum ggml_type        dst_type);
+
+    // a: [OC*IC, KD, KH, KW]
+    // b: [N*IC, ID, IH, IW]
+    // result: [N*OC, OD, OH, OW]
+    GGML_API struct ggml_tensor * ggml_conv_3d(
+                struct ggml_context * ctx,
+                struct ggml_tensor  * a,
+                struct ggml_tensor  * b,
+                int64_t               IC,
+                int                   s0, // stride width
+                int                   s1, // stride height
+                int                   s2, // stride depth
+                int                   p0, // padding width
+                int                   p1, // padding height
+                int                   p2, // padding depth
+                int                   d0, // dilation width
+                int                   d1, // dilation height
+                int                   d2  // dilation depth
+        );
+
+    // kernel size is a->ne[0] x a->ne[1]
+    // stride is equal to kernel size
+    // padding is zero
+    // example:
+    // a:     16   16    3  768
+    // b:   1024 1024    3    1
+    // res:   64   64  768    1
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // kernel size is a->ne[0] x a->ne[1]
+    // stride is 1
+    // padding is half
+    // example:
+    // a:      3    3    256  256
+    // b:     64   64    256    1
+    // res:   64   64    256    1
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // depthwise (via im2col and mul_mat)
+    GGML_API struct ggml_tensor * ggml_conv_2d_dw(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // convolution kernel
+            struct ggml_tensor  * b,  // data
+            int                  s0,  // stride dimension 0
+            int                  s1,  // stride dimension 1
+            int                  p0,  // padding dimension 0
+            int                  p1,  // padding dimension 1
+            int                  d0,  // dilation dimension 0
+            int                  d1); // dilation dimension 1
+
+    // Depthwise 2D convolution
+    // may be faster than ggml_conv_2d_dw, but not available in all backends
+    // a:   KW    KH    1    C    convolution kernel
+    // b:   W     H     C    N    input data
+    // res: W_out H_out C    N
+    GGML_API struct ggml_tensor * ggml_conv_2d_dw_direct(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   stride0,
+            int                   stride1,
+            int                   pad0,
+            int                   pad1,
+            int                   dilation0,
+            int                   dilation1);
+
+    GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   stride);
+
+    GGML_API struct ggml_tensor * ggml_conv_2d_direct(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // convolution kernel [KW, KH, IC, OC]
+            struct ggml_tensor  * b,   // input data [W, H, C, N]
+            int                   s0,  // stride dimension 0
+            int                   s1,  // stride dimension 1
+            int                   p0,  // padding dimension 0
+            int                   p1,  // padding dimension 1
+            int                   d0,  // dilation dimension 0
+            int                   d1); // dilation dimension 1
+
+    GGML_API struct ggml_tensor * ggml_conv_3d_direct(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,   // kernel [KW, KH, KD, IC * OC]
+            struct ggml_tensor  * b,   // input  [W, H, D, C * N]
+            int                   s0,  // stride
+            int                   s1,
+            int                   s2,
+            int                   p0,  // padding
+            int                   p1,
+            int                   p2,
+            int                   d0,  // dilation
+            int                   d1,
+            int                   d2,
+            int                   n_channels,
+            int                   n_batch,
+            int                   n_channels_out);
+
+    enum ggml_op_pool {
+        GGML_OP_POOL_MAX,
+        GGML_OP_POOL_AVG,
+        GGML_OP_POOL_COUNT,
+    };
+
+    GGML_API struct ggml_tensor * ggml_pool_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_op_pool     op,
+            int                   k0, // kernel size
+            int                   s0, // stride
+            int                   p0); // padding
+
+    // the result will have 2*p0 padding for the first dimension
+    // and 2*p1 padding for the second dimension
+    GGML_API struct ggml_tensor * ggml_pool_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_op_pool     op,
+            int                   k0,
+            int                   k1,
+            int                   s0,
+            int                   s1,
+            float                 p0,
+            float                 p1);
+
+    GGML_API struct ggml_tensor * ggml_pool_2d_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * af, // "a"/input used in forward pass
+            enum ggml_op_pool     op,
+            int                   k0,
+            int                   k1,
+            int                   s0,
+            int                   s1,
+            float                 p0,
+            float                 p1);
+
+    enum ggml_scale_mode {
+        GGML_SCALE_MODE_NEAREST  = 0,
+        GGML_SCALE_MODE_BILINEAR = 1,
+        GGML_SCALE_MODE_BICUBIC  = 2,
+
+        GGML_SCALE_MODE_COUNT
+    };
+
+    enum ggml_scale_flag {
+        GGML_SCALE_FLAG_ALIGN_CORNERS = (1 << 8),
+        GGML_SCALE_FLAG_ANTIALIAS     = (1 << 9),
+    };
+
+    // interpolate
+    // multiplies ne0 and ne1 by scale factor
+    GGML_API struct ggml_tensor * ggml_upscale(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   scale_factor,
+            enum ggml_scale_mode  mode);
+
+    // interpolate
+    // interpolate scale to specified dimensions
+    GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_upscale_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   ne0,
+            int                   ne1,
+            int                   ne2,
+            int                   ne3,
+            enum ggml_scale_mode  mode),
+        "use ggml_interpolate instead");
+
+    // Up- or downsamples the input to the specified size.
+    // 2D scale modes (eg. bilinear) are applied to the first two dimensions.
+    GGML_API struct ggml_tensor * ggml_interpolate(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3,
+            uint32_t              mode); // ggml_scale_mode [ | ggml_scale_flag...]
+
+    // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
+    GGML_API struct ggml_tensor * ggml_pad(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                  p0,
+            int                  p1,
+            int                  p2,
+            int                  p3);
+
+    // pad each dimension with values on the other side of the torus (looping around)
+    GGML_API struct ggml_tensor * ggml_pad_circular(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   p0,
+            int                   p1,
+            int                   p2,
+            int                   p3);
+
+    GGML_API struct ggml_tensor * ggml_pad_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                  lp0,
+            int                  rp0,
+            int                  lp1,
+            int                  rp1,
+            int                  lp2,
+            int                  rp2,
+            int                  lp3,
+            int                  rp3
+            );
+
+    // pad each dimension with values on the other side of the torus (looping around)
+    GGML_API struct ggml_tensor * ggml_pad_ext_circular(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   lp0,
+            int                   rp0,
+            int                   lp1,
+            int                   rp1,
+            int                   lp2,
+            int                   rp2,
+            int                   lp3,
+            int                   rp3);
+
+    // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
+    GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   p0,
+            int                   p1);
+
+    // Move tensor elements by an offset given for each dimension. Elements that
+    // are shifted beyond the last position are wrapped around to the beginning.
+    GGML_API struct ggml_tensor * ggml_roll(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   shift0,
+            int                   shift1,
+            int                   shift2,
+            int                   shift3);
+
+    // Convert matrix into a triangular one (upper, strict upper, lower or strict lower) by writing
+    // zeroes everywhere outside the masked area
+    GGML_API struct ggml_tensor * ggml_tri(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_tri_type    type);
+
+    // Fill tensor a with constant c
+    GGML_API struct ggml_tensor * ggml_fill(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 c);
+
+    GGML_API struct ggml_tensor * ggml_fill_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 c);
+
+    // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
+    // timesteps: [N,]
+    // return: [N, dim]
+    GGML_API struct ggml_tensor * ggml_timestep_embedding(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * timesteps,
+            int                   dim,
+            int                   max_period);
+
+    // sort rows
+    enum ggml_sort_order {
+        GGML_SORT_ORDER_ASC,
+        GGML_SORT_ORDER_DESC,
+    };
+
+    GGML_API struct ggml_tensor * ggml_argsort(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_sort_order  order);
+
+    // similar to ggml_top_k but implemented as `argsort` + `view`
+    GGML_API struct ggml_tensor * ggml_argsort_top_k(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   k);
+
+    // top k elements per row
+    // note: the resulting top k indices are in no particular order
+    GGML_API struct ggml_tensor * ggml_top_k(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   k);
+
+    GGML_API struct ggml_tensor * ggml_arange(
+            struct ggml_context * ctx,
+            float                 start,
+            float                 stop,
+            float                 step);
+
+    // q:    [n_embd_k, n_batch, n_head,    ne3 ]
+    // k:    [n_embd_k, n_kv,    n_head_kv, ne3 ]
+    // v:    [n_embd_v, n_kv,    n_head_kv, ne3 ] !! not transposed !!
+    // mask: [n_kv,     n_batch, ne32,      ne33]
+    // res:  [n_embd_v, n_head,  n_batch,   ne3 ] !! permuted !!
+    //
+    // broadcast:
+    //   n_head % n_head_kv == 0
+    //   n_head % ne32      == 0
+    //   ne3    % ne33      == 0
+    //
+    GGML_API struct ggml_tensor * ggml_flash_attn_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * mask,
+            float                 scale,
+            float                 max_bias,
+            float                 logit_softcap);
+
+    GGML_API void ggml_flash_attn_ext_set_prec(
+            struct ggml_tensor * a,
+            enum ggml_prec       prec);
+
+    GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec(
+            const struct ggml_tensor * a);
+
+    GGML_API void ggml_flash_attn_ext_add_sinks(
+            struct ggml_tensor * a,
+            struct ggml_tensor * sinks);
+
+    // TODO: needs to be adapted to ggml_flash_attn_ext
+    GGML_API struct ggml_tensor * ggml_flash_attn_back(
+           struct ggml_context * ctx,
+           struct ggml_tensor  * q,
+           struct ggml_tensor  * k,
+           struct ggml_tensor  * v,
+           struct ggml_tensor  * d,
+           bool                  masked);
+
+    GGML_API struct ggml_tensor * ggml_ssm_conv(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * sx,
+            struct ggml_tensor  * c);
+
+    GGML_API struct ggml_tensor * ggml_ssm_scan(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * s,
+            struct ggml_tensor  * x,
+            struct ggml_tensor  * dt,
+            struct ggml_tensor  * A,
+            struct ggml_tensor  * B,
+            struct ggml_tensor  * C,
+            struct ggml_tensor  * ids);
+
+    // partition into non-overlapping windows with padding if needed
+    // example:
+    // a:   768   64   64    1
+    // w:    14
+    // res: 768   14   14    25
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_win_part(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   w);
+
+    // reverse of ggml_win_part
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_win_unpart(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   w0,
+            int                   h0,
+            int                   w);
+
+    GGML_API struct ggml_tensor * ggml_unary(
+            struct ggml_context * ctx,
+             struct ggml_tensor * a,
+             enum ggml_unary_op op);
+
+    GGML_API struct ggml_tensor * ggml_unary_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        enum ggml_unary_op op);
+
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_get_rel_pos(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   qh,
+            int                   kh);
+
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_add_rel_pos(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * pw,
+            struct ggml_tensor  * ph);
+
+    GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * pw,
+            struct ggml_tensor  * ph);
+
+    GGML_API struct ggml_tensor * ggml_rwkv_wkv6(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * r,
+            struct ggml_tensor  * tf,
+            struct ggml_tensor  * td,
+            struct ggml_tensor  * state);
+
+    GGML_API struct ggml_tensor * ggml_gated_linear_attn(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * g,
+            struct ggml_tensor  * state,
+            float scale);
+
+    GGML_API struct ggml_tensor * ggml_rwkv_wkv7(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * r,
+            struct ggml_tensor  * w,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * state);
+
+    /* Solves a specific equation of the form Ax=B, where A is a triangular matrix
+    *  without zeroes on the diagonal (i.e. invertible).
+    *  B can have any number of columns, but must have the same number of rows as A
+    *  If A is [n, n] and B is [n, m], then the result will be [n, m] as well
+    *  Has O(n^3) complexity (unlike most matrix ops out there), so use on cases
+    *  where n > 100 sparingly, pre-chunk if necessary.
+    *
+    *  If left = false, solves xA=B instead
+    *  If lower = false, assumes upper triangular instead
+    *  If uni = true, assumes diagonal of A to be all ones (will override actual values)
+    *
+    *  TODO: currently only lower, right, non-unitriangular variant is implemented
+    */
+    GGML_API struct ggml_tensor * ggml_solve_tri(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        bool                  left,
+        bool                  lower,
+        bool                  uni);
+
+    // custom operators
+
+    typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
+    typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
+
+#define GGML_N_TASKS_MAX (-1)
+    // n_tasks == GGML_N_TASKS_MAX means to use max number of tasks
+
+    GGML_API struct ggml_tensor * ggml_map_custom1(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            ggml_custom1_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            ggml_custom2_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
+            struct ggml_context   * ctx,
+            struct ggml_tensor    * a,
+            struct ggml_tensor    * b,
+            struct ggml_tensor    * c,
+            ggml_custom3_op_t       fun,
+            int                     n_tasks,
+            void                  * userdata);
+
+    typedef void (*ggml_custom_op_t)(struct ggml_tensor * dst , int ith, int nth, void * userdata);
+
+    GGML_API struct ggml_tensor * ggml_custom_4d(
+            struct ggml_context * ctx,
+            enum ggml_type        type,
+            int64_t               ne0,
+            int64_t               ne1,
+            int64_t               ne2,
+            int64_t               ne3,
+            struct ggml_tensor ** args,
+            int                   n_args,
+            ggml_custom_op_t      fun,
+            int                   n_tasks,
+            void                * userdata);
+
+    GGML_API struct ggml_tensor * ggml_custom_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor ** args,
+            int                   n_args,
+            ggml_custom_op_t      fun,
+            int                   n_tasks,
+            void                * userdata);
+
+    // loss function
+
+    GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // logits
+            struct ggml_tensor  * b); // labels
+
+    GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,  // logits
+            struct ggml_tensor  * b,  // labels
+            struct ggml_tensor  * c); // gradients of cross_entropy_loss result
+
+    // AdamW optimizer step
+    // Paper: https://arxiv.org/pdf/1711.05101v3.pdf
+    // PyTorch: https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html
+    GGML_API struct ggml_tensor * ggml_opt_step_adamw(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * grad,
+            struct ggml_tensor  * m,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * adamw_params); // parameters such as the learning rate
+
+    // stochastic gradient descent step (with weight decay)
+    GGML_API struct ggml_tensor * ggml_opt_step_sgd(
+        struct ggml_context * ctx,
+        struct ggml_tensor *  a,
+        struct ggml_tensor *  grad,
+        struct ggml_tensor *  sgd_params); // alpha, weight decay
+
+    //
+    // automatic differentiation
+    //
+
+    GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
+    GGML_API void ggml_build_backward_expand(
+        struct ggml_context *  ctx,        // context for gradient computation
+        struct ggml_cgraph  *  cgraph,
+        struct ggml_tensor  ** grad_accs);
+
+    // graph allocation in a context
+    GGML_API struct ggml_cgraph * ggml_new_graph       (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
+    GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
+    GGML_API struct ggml_cgraph * ggml_graph_dup       (struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads);
+    GGML_API void                 ggml_graph_cpy       (struct ggml_cgraph * src, struct ggml_cgraph * dst);
+    GGML_API void                 ggml_graph_reset     (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
+    GGML_API void                 ggml_graph_clear     (struct ggml_cgraph * cgraph);
+
+    GGML_API int                   ggml_graph_size   (struct ggml_cgraph * cgraph);
+    GGML_API struct ggml_tensor *  ggml_graph_node   (struct ggml_cgraph * cgraph, int i); // if i < 0, returns nodes[n_nodes + i]
+    GGML_API struct ggml_tensor ** ggml_graph_nodes  (struct ggml_cgraph * cgraph);
+    GGML_API int                   ggml_graph_n_nodes(struct ggml_cgraph * cgraph);
+
+    GGML_API void   ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
+
+    GGML_API size_t ggml_graph_overhead(void);
+    GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads);
+
+    GGML_API struct ggml_tensor * ggml_graph_get_tensor  (const struct ggml_cgraph * cgraph, const char * name);
+    GGML_API struct ggml_tensor * ggml_graph_get_grad    (const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);
+    GGML_API struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node);
+
+    // print info and performance information for the graph
+    GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);
+
+    // dump the graph into a file using the dot format
+    GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
+
+    // TODO these functions were sandwiched in the old optimization interface, is there a better place for them?
+    typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data);
+
+    // Set callback for all future logging events.
+    // If this is not called, or NULL is supplied, everything is output on stderr.
+    GGML_API void ggml_log_get(ggml_log_callback * log_callback, void ** user_data);
+    GGML_API void ggml_log_set(ggml_log_callback   log_callback, void *  user_data);
+
+    GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
+
+    //
+    // quantization
+    //
+
+    // - ggml_quantize_init can be called multiple times with the same type
+    //   it will only initialize the quantization tables for the first call or after ggml_quantize_free
+    //   automatically called by ggml_quantize_chunk for convenience
+    //
+    // - ggml_quantize_free will free any memory allocated by ggml_quantize_init
+    //   call this at the end of the program to avoid memory leaks
+    //
+    // note: these are thread-safe
+    //
+    GGML_API void ggml_quantize_init(enum ggml_type type);
+    GGML_API void ggml_quantize_free(void);
+
+    // some quantization type cannot be used without an importance matrix
+    GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type);
+
+    // calls ggml_quantize_init internally (i.e. can allocate memory)
+    GGML_API size_t ggml_quantize_chunk(
+            enum ggml_type   type,
+               const float * src,
+                      void * dst,
+                   int64_t   start,
+                   int64_t   nrows,
+                   int64_t   n_per_row,
+               const float * imatrix);
+
+#ifdef __cplusplus
+    // restrict not standard in C++
+#    if defined(__GNUC__)
+#        define GGML_RESTRICT __restrict__
+#    elif defined(__clang__)
+#        define GGML_RESTRICT __restrict
+#    elif defined(_MSC_VER)
+#        define GGML_RESTRICT __restrict
+#    else
+#        define GGML_RESTRICT
+#    endif
+#else
+#    if defined (_MSC_VER) && (__STDC_VERSION__ < 201112L)
+#        define GGML_RESTRICT __restrict
+#    else
+#        define GGML_RESTRICT restrict
+#    endif
+#endif
+    typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+    typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t k);
+
+    struct ggml_type_traits {
+        const char             * type_name;
+        int64_t                  blck_size;
+        int64_t                  blck_size_interleave; // interleave elements in blocks
+        size_t                   type_size;
+        bool                     is_quantized;
+        ggml_to_float_t          to_float;
+        ggml_from_float_t        from_float_ref;
+    };
+
+    GGML_API const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type);
+
+    // ggml threadpool
+    // TODO: currently, only a few functions are in the base ggml API, while the rest are in the CPU backend
+    // the goal should be to create an API that other backends can use move everything to the ggml base
+
+    // scheduling priorities
+    enum ggml_sched_priority {
+        GGML_SCHED_PRIO_LOW = -1,
+        GGML_SCHED_PRIO_NORMAL,
+        GGML_SCHED_PRIO_MEDIUM,
+        GGML_SCHED_PRIO_HIGH,
+        GGML_SCHED_PRIO_REALTIME
+    };
+
+    // threadpool params
+    // Use ggml_threadpool_params_default() or ggml_threadpool_params_init() to populate the defaults
+    struct ggml_threadpool_params {
+        bool                cpumask[GGML_MAX_N_THREADS]; // mask of cpu cores (all-zeros means use default affinity settings)
+        int                 n_threads;                   // number of threads
+        enum ggml_sched_priority prio;                   // thread priority
+        uint32_t            poll;                        // polling level (0 - no polling, 100 - aggressive polling)
+        bool                strict_cpu;                  // strict cpu placement
+        bool                paused;                      // start in paused state
+    };
+
+    struct ggml_threadpool;     // forward declaration, see ggml.c
+
+    typedef struct ggml_threadpool * ggml_threadpool_t;
+
+    GGML_API struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads);
+    GGML_API void                          ggml_threadpool_params_init   (struct ggml_threadpool_params * p, int n_threads);
+    GGML_API bool                          ggml_threadpool_params_match  (const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/ggml/include/gguf.h b/ggml/whispercpp/third_party/ggml/include/gguf.h
new file mode 100644
index 0000000..79ee202
--- /dev/null
+++ b/ggml/whispercpp/third_party/ggml/include/gguf.h
@@ -0,0 +1,202 @@
+// This file contains functionality related to "GGUF" files, the binary file format used by ggml.
+// GGUF files have the following structure:
+//
+// 1. File magic "GGUF" (4 bytes).
+// 2. File version (uint32_t).
+// 3. Number of ggml tensors in file (int64_t).
+// 4. Number of key-value-pairs in file (int64_t).
+// 5. For each KV pair:
+//   1. The key (string).
+//   2. The value type (gguf_type).
+//   3a. If the value type is GGUF_TYPE_ARRAY:
+//     1. The type of the array (gguf_type).
+//     2. The number of elements in the array (uint64_t).
+//     3. The binary representation of each element in the array.
+//   3b. Otherwise:
+//     1. The binary representation of the value.
+// 6. For each ggml tensor:
+//   1. The tensor name (string).
+//   2. The number of dimensions of the tensor (uint32_t).
+//   3. For each dimension:
+//     1. The size of the tensor in the dimension (int64_t).
+//   4. The tensor data type (ggml_type).
+//   5. The tensor data offset in the tensor data binary blob (uint64_t).
+// 7. The tensor data binary blob (optional, aligned).
+//
+// Strings are serialized as the string length (uint64_t) followed by the C string without the null terminator.
+// All enums are stored as int32_t.
+// All bool values are stored as int8_t.
+// If the special key "general.alignment" (uint32_t) is defined it is used for alignment,
+//   otherwise GGUF_DEFAULT_ALIGNMENT is used.
+//
+// Module maintainer: Johannes Gäßler (@JohannesGaessler, johannesg@5d6.de)
+
+#pragma once
+
+#include "ggml.h"
+
+#include <stdbool.h>
+#include <stdint.h>
+
+#define GGUF_MAGIC   "GGUF"
+#define GGUF_VERSION 3
+
+#define GGUF_KEY_GENERAL_ALIGNMENT "general.alignment"
+
+#define GGUF_DEFAULT_ALIGNMENT 32
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+    // types that can be stored as GGUF KV data
+    enum gguf_type {
+        GGUF_TYPE_UINT8   = 0,
+        GGUF_TYPE_INT8    = 1,
+        GGUF_TYPE_UINT16  = 2,
+        GGUF_TYPE_INT16   = 3,
+        GGUF_TYPE_UINT32  = 4,
+        GGUF_TYPE_INT32   = 5,
+        GGUF_TYPE_FLOAT32 = 6,
+        GGUF_TYPE_BOOL    = 7,
+        GGUF_TYPE_STRING  = 8,
+        GGUF_TYPE_ARRAY   = 9,
+        GGUF_TYPE_UINT64  = 10,
+        GGUF_TYPE_INT64   = 11,
+        GGUF_TYPE_FLOAT64 = 12,
+        GGUF_TYPE_COUNT,       // marks the end of the enum
+    };
+
+    struct gguf_context;
+
+    struct gguf_init_params {
+        bool no_alloc;
+
+        // if not NULL, create a ggml_context and allocate the tensor data in it
+        struct ggml_context ** ctx;
+    };
+
+    GGML_API struct gguf_context * gguf_init_empty(void);
+    GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
+    //GGML_API struct gguf_context * gguf_init_from_buffer(..);
+
+    GGML_API void gguf_free(struct gguf_context * ctx);
+
+    GGML_API const char * gguf_type_name(enum gguf_type type);
+
+    GGML_API uint32_t gguf_get_version    (const struct gguf_context * ctx);
+    GGML_API size_t   gguf_get_alignment  (const struct gguf_context * ctx);
+    GGML_API size_t   gguf_get_data_offset(const struct gguf_context * ctx);
+
+    GGML_API int64_t      gguf_get_n_kv(const struct gguf_context * ctx);
+    GGML_API int64_t      gguf_find_key(const struct gguf_context * ctx, const char * key); // returns -1 if key is not found
+    GGML_API const char * gguf_get_key (const struct gguf_context * ctx, int64_t key_id);
+
+    GGML_API enum gguf_type gguf_get_kv_type (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id);
+
+    // will abort if the wrong type is used for the key
+    GGML_API uint8_t      gguf_get_val_u8  (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int8_t       gguf_get_val_i8  (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API uint16_t     gguf_get_val_u16 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int16_t      gguf_get_val_i16 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API uint32_t     gguf_get_val_u32 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int32_t      gguf_get_val_i32 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API float        gguf_get_val_f32 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API uint64_t     gguf_get_val_u64 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API int64_t      gguf_get_val_i64 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API double       gguf_get_val_f64 (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API bool         gguf_get_val_bool(const struct gguf_context * ctx, int64_t key_id);
+    GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int64_t key_id);
+    GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id);
+    GGML_API size_t       gguf_get_arr_n   (const struct gguf_context * ctx, int64_t key_id);
+
+    // get raw pointer to the first element of the array with the given key_id
+    // for bool arrays, note that they are always stored as int8 on all platforms (usually this makes no difference)
+    GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id);
+
+    // get ith C string from array with given key_id
+    GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int64_t key_id, size_t i);
+
+    GGML_API int64_t        gguf_get_n_tensors    (const struct gguf_context * ctx);
+    GGML_API int64_t        gguf_find_tensor      (const struct gguf_context * ctx, const char * name); // returns -1 if the tensor is not found
+    GGML_API size_t         gguf_get_tensor_offset(const struct gguf_context * ctx, int64_t tensor_id);
+    GGML_API const char *   gguf_get_tensor_name  (const struct gguf_context * ctx, int64_t tensor_id);
+    GGML_API enum ggml_type gguf_get_tensor_type  (const struct gguf_context * ctx, int64_t tensor_id);
+    GGML_API size_t         gguf_get_tensor_size  (const struct gguf_context * ctx, int64_t tensor_id);
+
+    // removes key if it exists, returns id that the key had prior to removal (-1 if it didn't exist)
+    GGML_API int64_t gguf_remove_key(struct gguf_context * ctx, const char * key);
+
+    // overrides an existing KV pair or adds a new one, the new KV pair is always at the back
+    GGML_API void gguf_set_val_u8  (struct gguf_context * ctx, const char * key, uint8_t      val);
+    GGML_API void gguf_set_val_i8  (struct gguf_context * ctx, const char * key, int8_t       val);
+    GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t     val);
+    GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t      val);
+    GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t     val);
+    GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t      val);
+    GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float        val);
+    GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t     val);
+    GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t      val);
+    GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double       val);
+    GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool         val);
+    GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
+
+    // creates a new array with n elements of the given type and copies the corresponding number of bytes from data
+    GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, size_t n);
+
+    // creates a new array with n strings and copies the corresponding strings from data
+    GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, size_t n);
+
+    // set or add KV pairs from another context
+    GGML_API void gguf_set_kv(struct gguf_context * ctx, const struct gguf_context * src);
+
+    // add tensor to GGUF context, tensor name must be unique
+    GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
+
+    // after changing a tensor's type, the offsets of all tensors with higher indices are immediately recalculated
+    //   in such a way that the tensor data remains as one contiguous block (except for padding)
+    GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
+
+    // assumes that at least gguf_get_tensor_size bytes can be read from data
+    GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data);
+
+    // writing gguf files can be done in 3 ways:
+    //
+    // - write the entire gguf_context to a binary file in a single pass:
+    //
+    //   gguf_write_to_file(ctx, fname, /*only_meta =*/ false);
+    //
+    // - write only the meta data to a file, then re-open the file and append the tensor data:
+    //
+    //   gguf_write_to_file(ctx, fname, /*only_meta =*/ true);
+    //   FILE * f = fopen(fname, "ab");
+    //   fwrite(f, ...); // write tensor data
+    //   fclose(f);
+    //
+    // - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
+    //
+    //   FILE * f = fopen(fname, "wb");
+    //   const size_t size_meta = gguf_get_meta_size(ctx);
+    //   fseek(f, size_meta, SEEK_SET);
+    //   fwrite(f, ...); // write tensor data
+    //   void * data = malloc(size_meta);
+    //   gguf_get_meta_data(ctx, data);
+    //   rewind(f);
+    //   fwrite(data, 1, data, f);
+    //   free(data);
+    //   fclose(f);
+    //
+
+    // write the entire context to a binary file
+    GGML_API bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta);
+
+    // get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
+    GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
+
+    // writes the meta data to pointer "data"
+    GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);
+
+#ifdef  __cplusplus
+}
+#endif
diff --git a/ggml/whispercpp/third_party/include/whisper.h b/ggml/whispercpp/third_party/include/whisper.h
new file mode 100644
index 0000000..f4cc6bf
--- /dev/null
+++ b/ggml/whispercpp/third_party/include/whisper.h
@@ -0,0 +1,741 @@
+#ifndef WHISPER_H
+#define WHISPER_H
+
+#include "ggml.h"
+#include "ggml-cpu.h"
+
+#include <stddef.h>
+#include <stdint.h>
+#include <stdbool.h>
+
+#ifdef __GNUC__
+#    define WHISPER_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
+#elif defined(_MSC_VER)
+#    define WHISPER_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define WHISPER_DEPRECATED(func, hint) func
+#endif
+
+#ifdef WHISPER_SHARED
+#    ifdef _WIN32
+#        ifdef WHISPER_BUILD
+#            define WHISPER_API __declspec(dllexport)
+#        else
+#            define WHISPER_API __declspec(dllimport)
+#        endif
+#    else
+#        define WHISPER_API __attribute__ ((visibility ("default")))
+#    endif
+#else
+#    define WHISPER_API
+#endif
+
+#define WHISPER_SAMPLE_RATE 16000
+#define WHISPER_N_FFT       400
+#define WHISPER_HOP_LENGTH  160
+#define WHISPER_CHUNK_SIZE  30
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+    //
+    // C interface
+    //
+    // The following interface is thread-safe as long as the sample whisper_context is not used by multiple threads
+    // concurrently.
+    //
+    // Basic usage:
+    //
+    //     #include "whisper.h"
+    //
+    //     ...
+    //
+    //     whisper_context_params cparams = whisper_context_default_params();
+    //
+    //     struct whisper_context * ctx = whisper_init_from_file_with_params("/path/to/ggml-base.en.bin", cparams);
+    //
+    //     if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+    //         fprintf(stderr, "failed to process audio\n");
+    //         return 7;
+    //     }
+    //
+    //     const int n_segments = whisper_full_n_segments(ctx);
+    //     for (int i = 0; i < n_segments; ++i) {
+    //         const char * text = whisper_full_get_segment_text(ctx, i);
+    //         printf("%s", text);
+    //     }
+    //
+    //     whisper_free(ctx);
+    //
+    //     ...
+    //
+    // This is a demonstration of the most straightforward usage of the library.
+    // "pcmf32" contains the RAW audio data in 32-bit floating point format.
+    //
+    // The interface also allows for more fine-grained control over the computation, but it requires a deeper
+    // understanding of how the model works.
+    //
+
+    struct whisper_context;
+    struct whisper_state;
+    struct whisper_full_params;
+
+    typedef int32_t whisper_pos;
+    typedef int32_t whisper_token;
+    typedef int32_t whisper_seq_id;
+
+    enum whisper_alignment_heads_preset {
+        WHISPER_AHEADS_NONE,
+        WHISPER_AHEADS_N_TOP_MOST,  // All heads from the N-top-most text-layers
+        WHISPER_AHEADS_CUSTOM,
+        WHISPER_AHEADS_TINY_EN,
+        WHISPER_AHEADS_TINY,
+        WHISPER_AHEADS_BASE_EN,
+        WHISPER_AHEADS_BASE,
+        WHISPER_AHEADS_SMALL_EN,
+        WHISPER_AHEADS_SMALL,
+        WHISPER_AHEADS_MEDIUM_EN,
+        WHISPER_AHEADS_MEDIUM,
+        WHISPER_AHEADS_LARGE_V1,
+        WHISPER_AHEADS_LARGE_V2,
+        WHISPER_AHEADS_LARGE_V3,
+        WHISPER_AHEADS_LARGE_V3_TURBO,
+    };
+
+    typedef struct whisper_ahead {
+        int n_text_layer;
+        int n_head;
+    } whisper_ahead;
+
+    typedef struct whisper_aheads {
+        size_t n_heads;
+        const whisper_ahead * heads;
+    } whisper_aheads;
+
+    struct whisper_context_params {
+        bool  use_gpu;
+        bool  flash_attn;
+        int   gpu_device;  // CUDA device
+
+        // [EXPERIMENTAL] Token-level timestamps with DTW
+        bool dtw_token_timestamps;
+        enum whisper_alignment_heads_preset dtw_aheads_preset;
+
+        int dtw_n_top;
+        struct whisper_aheads dtw_aheads;
+
+        size_t dtw_mem_size; // TODO: remove
+    };
+
+    typedef struct whisper_token_data {
+        whisper_token id;  // token id
+        whisper_token tid; // forced timestamp token id
+
+        float p;           // probability of the token
+        float plog;        // log probability of the token
+        float pt;          // probability of the timestamp token
+        float ptsum;       // sum of probabilities of all timestamp tokens
+
+        // token-level timestamp data
+        // do not use if you haven't computed token-level timestamps
+        int64_t t0;        // start time of the token
+        int64_t t1;        //   end time of the token
+
+        // [EXPERIMENTAL] Token-level timestamps with DTW
+        // do not use if you haven't computed token-level timestamps with dtw
+        // Roughly corresponds to the moment in audio in which the token was output
+        int64_t t_dtw;
+
+        float vlen;        // voice length of the token
+    } whisper_token_data;
+
+    typedef struct whisper_model_loader {
+        void * context;
+
+        size_t (*read)(void * ctx, void * output, size_t read_size);
+        bool    (*eof)(void * ctx);
+        void  (*close)(void * ctx);
+    } whisper_model_loader;
+
+    // grammar element type
+    enum whisper_gretype {
+        // end of rule definition
+        WHISPER_GRETYPE_END            = 0,
+
+        // start of alternate definition for rule
+        WHISPER_GRETYPE_ALT            = 1,
+
+        // non-terminal element: reference to rule
+        WHISPER_GRETYPE_RULE_REF       = 2,
+
+        // terminal element: character (code point)
+        WHISPER_GRETYPE_CHAR           = 3,
+
+        // inverse char(s) ([^a], [^a-b] [^abc])
+        WHISPER_GRETYPE_CHAR_NOT       = 4,
+
+        // modifies a preceding WHISPER_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
+        // be an inclusive range ([a-z])
+        WHISPER_GRETYPE_CHAR_RNG_UPPER = 5,
+
+        // modifies a preceding WHISPER_GRETYPE_CHAR or
+        // WHISPER_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
+        WHISPER_GRETYPE_CHAR_ALT       = 6,
+    };
+
+    typedef struct whisper_grammar_element {
+        enum whisper_gretype type;
+        uint32_t             value; // Unicode code point or rule ID
+    } whisper_grammar_element;
+
+    typedef struct whisper_vad_params {
+        float threshold;               // Probability threshold to consider as speech.
+        int   min_speech_duration_ms;  // Min duration for a valid speech segment.
+        int   min_silence_duration_ms; // Min silence duration to consider speech as ended.
+        float max_speech_duration_s;   // Max duration of a speech segment before forcing a new segment.
+        int   speech_pad_ms;           // Padding added before and after speech segments.
+        float samples_overlap;         // Overlap in seconds when copying audio samples from speech segment.
+    } whisper_vad_params;
+
+    WHISPER_API const char * whisper_version(void);
+
+    // Various functions for loading a ggml whisper model.
+    // Allocate (almost) all memory needed for the model.
+    // Return NULL on failure
+    WHISPER_API struct whisper_context * whisper_init_from_file_with_params  (const char * path_model,              struct whisper_context_params params);
+    WHISPER_API struct whisper_context * whisper_init_from_buffer_with_params(void * buffer, size_t buffer_size,    struct whisper_context_params params);
+    WHISPER_API struct whisper_context * whisper_init_with_params            (struct whisper_model_loader * loader, struct whisper_context_params params);
+
+    // These are the same as the above, but the internal state of the context is not allocated automatically
+    // It is the responsibility of the caller to allocate the state using whisper_init_state() (#523)
+    WHISPER_API struct whisper_context * whisper_init_from_file_with_params_no_state  (const char * path_model,              struct whisper_context_params params);
+    WHISPER_API struct whisper_context * whisper_init_from_buffer_with_params_no_state(void * buffer, size_t buffer_size,    struct whisper_context_params params);
+    WHISPER_API struct whisper_context * whisper_init_with_params_no_state            (struct whisper_model_loader * loader, struct whisper_context_params params);
+
+    WHISPER_DEPRECATED(
+        WHISPER_API struct whisper_context * whisper_init_from_file(const char * path_model),
+        "use whisper_init_from_file_with_params instead"
+    );
+    WHISPER_DEPRECATED(
+        WHISPER_API struct whisper_context * whisper_init_from_buffer(void * buffer, size_t buffer_size),
+        "use whisper_init_from_buffer_with_params instead"
+    );
+    WHISPER_DEPRECATED(
+        WHISPER_API struct whisper_context * whisper_init(struct whisper_model_loader * loader),
+        "use whisper_init_with_params instead"
+    );
+    WHISPER_DEPRECATED(
+        WHISPER_API struct whisper_context * whisper_init_from_file_no_state(const char * path_model),
+        "use whisper_init_from_file_with_params_no_state instead"
+    );
+    WHISPER_DEPRECATED(
+        WHISPER_API struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t buffer_size),
+        "use whisper_init_from_buffer_with_params_no_state instead"
+    );
+    WHISPER_DEPRECATED(
+        WHISPER_API struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loader),
+        "use whisper_init_with_params_no_state instead"
+    );
+
+    WHISPER_API struct whisper_state * whisper_init_state(struct whisper_context * ctx);
+
+    // Given a context, enable use of OpenVINO for encode inference.
+    // model_path: Optional path to OpenVINO encoder IR model. If set to nullptr,
+    //                      the path will be generated from the ggml model path that was passed
+    //                      in to whisper_init_from_file. For example, if 'path_model' was
+    //                      "/path/to/ggml-base.en.bin", then OpenVINO IR model path will be
+    //                      assumed to be "/path/to/ggml-base.en-encoder-openvino.xml".
+    // device: OpenVINO device to run inference on ("CPU", "GPU", etc.)
+    // cache_dir: Optional cache directory that can speed up init time, especially for
+    //                     GPU, by caching compiled 'blobs' there.
+    //                     Set to nullptr if not used.
+    // Returns 0 on success. If OpenVINO is not enabled in build, this simply returns 1.
+    WHISPER_API int whisper_ctx_init_openvino_encoder_with_state(
+        struct whisper_context * ctx,
+          struct whisper_state * state,
+                    const char * model_path,
+                    const char * device,
+                    const char * cache_dir);
+
+    WHISPER_API int whisper_ctx_init_openvino_encoder(
+        struct whisper_context * ctx,
+                    const char * model_path,
+                    const char * device,
+                    const char * cache_dir);
+
+    // Frees all allocated memory
+    WHISPER_API void whisper_free      (struct whisper_context * ctx);
+    WHISPER_API void whisper_free_state(struct whisper_state * state);
+    WHISPER_API void whisper_free_params(struct whisper_full_params * params);
+    WHISPER_API void whisper_free_context_params(struct whisper_context_params * params);
+
+    // Convert RAW PCM audio to log mel spectrogram.
+    // The resulting spectrogram is stored inside the default state of the provided whisper context.
+    // Returns 0 on success
+    WHISPER_API int whisper_pcm_to_mel(
+            struct whisper_context * ctx,
+                       const float * samples,
+                               int   n_samples,
+                               int   n_threads);
+
+    WHISPER_API int whisper_pcm_to_mel_with_state(
+            struct whisper_context * ctx,
+              struct whisper_state * state,
+                       const float * samples,
+                               int   n_samples,
+                               int   n_threads);
+
+    // This can be used to set a custom log mel spectrogram inside the default state of the provided whisper context.
+    // Use this instead of whisper_pcm_to_mel() if you want to provide your own log mel spectrogram.
+    // n_mel must be 80
+    // Returns 0 on success
+    WHISPER_API int whisper_set_mel(
+            struct whisper_context * ctx,
+                       const float * data,
+                               int   n_len,
+                               int   n_mel);
+
+    WHISPER_API int whisper_set_mel_with_state(
+            struct whisper_context * ctx,
+              struct whisper_state * state,
+                       const float * data,
+                               int   n_len,
+                               int   n_mel);
+
+    // Run the Whisper encoder on the log mel spectrogram stored inside the default state in the provided whisper context.
+    // Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first.
+    // offset can be used to specify the offset of the first frame in the spectrogram.
+    // Returns 0 on success
+    WHISPER_API int whisper_encode(
+            struct whisper_context * ctx,
+                               int   offset,
+                               int   n_threads);
+
+    WHISPER_API int whisper_encode_with_state(
+            struct whisper_context * ctx,
+              struct whisper_state * state,
+                               int   offset,
+                               int   n_threads);
+
+    // Run the Whisper decoder to obtain the logits and probabilities for the next token.
+    // Make sure to call whisper_encode() first.
+    // tokens + n_tokens is the provided context for the decoder.
+    // n_past is the number of tokens to use from previous decoder calls.
+    // Returns 0 on success
+    // TODO: add support for multiple decoders
+    WHISPER_API int whisper_decode(
+            struct whisper_context * ctx,
+               const whisper_token * tokens,
+                               int   n_tokens,
+                               int   n_past,
+                               int   n_threads);
+
+    WHISPER_API int whisper_decode_with_state(
+            struct whisper_context * ctx,
+              struct whisper_state * state,
+               const whisper_token * tokens,
+                               int   n_tokens,
+                               int   n_past,
+                               int   n_threads);
+
+    // Convert the provided text into tokens.
+    // The tokens pointer must be large enough to hold the resulting tokens.
+    // Returns the number of tokens on success, no more than n_max_tokens
+    // Returns a negative number on failure - the number of tokens that would have been returned
+    // TODO: not sure if correct
+    WHISPER_API int whisper_tokenize(
+            struct whisper_context * ctx,
+                        const char * text,
+                     whisper_token * tokens,
+                               int   n_max_tokens);
+
+    // Return the number of tokens in the provided text
+    // Equivalent to: -whisper_tokenize(ctx, text, NULL, 0)
+    int whisper_token_count(struct whisper_context * ctx, const char * text);
+
+    // Largest language id (i.e. number of available languages - 1)
+    WHISPER_API int whisper_lang_max_id(void);
+
+    // Return the id of the specified language, returns -1 if not found
+    // Examples:
+    //   "de" -> 2
+    //   "german" -> 2
+    WHISPER_API int whisper_lang_id(const char * lang);
+
+    // Return the short string of the specified language id (e.g. 2 -> "de"), returns nullptr if not found
+    WHISPER_API const char * whisper_lang_str(int id);
+
+    // Return the short string of the specified language name (e.g. 2 -> "german"), returns nullptr if not found
+    WHISPER_API const char * whisper_lang_str_full(int id);
+
+    // Use mel data at offset_ms to try and auto-detect the spoken language
+    // Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first
+    // Returns the top language id or negative on failure
+    // If not null, fills the lang_probs array with the probabilities of all languages
+    // The array must be whisper_lang_max_id() + 1 in size
+    // ref: https://github.com/openai/whisper/blob/main/whisper/decoding.py#L18-L69
+    WHISPER_API int whisper_lang_auto_detect(
+            struct whisper_context * ctx,
+                               int   offset_ms,
+                               int   n_threads,
+                             float * lang_probs);
+
+    WHISPER_API int whisper_lang_auto_detect_with_state(
+            struct whisper_context * ctx,
+              struct whisper_state * state,
+                               int   offset_ms,
+                               int   n_threads,
+                             float * lang_probs);
+
+    WHISPER_API int whisper_n_len           (struct whisper_context * ctx); // mel length
+    WHISPER_API int whisper_n_len_from_state(struct whisper_state * state); // mel length
+    WHISPER_API int whisper_n_vocab         (struct whisper_context * ctx);
+    WHISPER_API int whisper_n_text_ctx      (struct whisper_context * ctx);
+    WHISPER_API int whisper_n_audio_ctx     (struct whisper_context * ctx);
+    WHISPER_API int whisper_is_multilingual (struct whisper_context * ctx);
+
+    WHISPER_API int whisper_model_n_vocab      (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_audio_ctx  (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_audio_state(struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_audio_head (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_audio_layer(struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_text_ctx   (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_text_state (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_text_head  (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_text_layer (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_n_mels       (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_ftype        (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_type         (struct whisper_context * ctx);
+
+    // Token logits obtained from the last call to whisper_decode()
+    // The logits for the last token are stored in the last row
+    // Rows: n_tokens
+    // Cols: n_vocab
+    WHISPER_API float * whisper_get_logits           (struct whisper_context * ctx);
+    WHISPER_API float * whisper_get_logits_from_state(struct whisper_state * state);
+
+    // Token Id -> String. Uses the vocabulary in the provided context
+    WHISPER_API const char * whisper_token_to_str(struct whisper_context * ctx, whisper_token token);
+    WHISPER_API const char * whisper_model_type_readable(struct whisper_context * ctx);
+
+
+    // Special tokens
+    WHISPER_API whisper_token whisper_token_eot (struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_sot (struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_solm(struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_prev(struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_nosp(struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_not (struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_beg (struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_lang(struct whisper_context * ctx, int lang_id);
+
+    // Task tokens
+    WHISPER_API whisper_token whisper_token_translate (struct whisper_context * ctx);
+    WHISPER_API whisper_token whisper_token_transcribe(struct whisper_context * ctx);
+
+    // Performance information from the default state.
+    struct whisper_timings {
+        float sample_ms;
+        float encode_ms;
+        float decode_ms;
+        float batchd_ms;
+        float prompt_ms;
+    };
+    WHISPER_API struct whisper_timings * whisper_get_timings(struct whisper_context * ctx);
+    WHISPER_API void whisper_print_timings(struct whisper_context * ctx);
+    WHISPER_API void whisper_reset_timings(struct whisper_context * ctx);
+
+    // Print system information
+    WHISPER_API const char * whisper_print_system_info(void);
+
+    ////////////////////////////////////////////////////////////////////////////
+
+    // Available sampling strategies
+    enum whisper_sampling_strategy {
+        WHISPER_SAMPLING_GREEDY,      // similar to OpenAI's GreedyDecoder
+        WHISPER_SAMPLING_BEAM_SEARCH, // similar to OpenAI's BeamSearchDecoder
+    };
+
+    // Text segment callback
+    // Called on every newly generated text segment
+    // Use the whisper_full_...() functions to obtain the text segments
+    typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data);
+
+    // Progress callback
+    typedef void (*whisper_progress_callback)(struct whisper_context * ctx, struct whisper_state * state, int progress, void * user_data);
+
+    // Encoder begin callback
+    // If not NULL, called before the encoder starts
+    // If it returns false, the computation is aborted
+    typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, struct whisper_state * state, void * user_data);
+
+    // Logits filter callback
+    // Can be used to modify the logits before sampling
+    // If not NULL, called after applying temperature to logits
+    typedef void (*whisper_logits_filter_callback)(
+            struct whisper_context * ctx,
+              struct whisper_state * state,
+          const whisper_token_data * tokens,
+                               int   n_tokens,
+                             float * logits,
+                              void * user_data);
+
+    // Parameters for the whisper_full() function
+    // If you change the order or add new parameters, make sure to update the default values in whisper.cpp:
+    // whisper_full_default_params()
+    struct whisper_full_params {
+        enum whisper_sampling_strategy strategy;
+
+        int n_threads;
+        int n_max_text_ctx;     // max tokens to use from past text as prompt for the decoder
+        int offset_ms;          // start offset in ms
+        int duration_ms;        // audio duration to process in ms
+
+        bool translate;
+        bool no_context;        // do not use past transcription (if any) as initial prompt for the decoder
+        bool no_timestamps;     // do not generate timestamps
+        bool single_segment;    // force single segment output (useful for streaming)
+        bool print_special;     // print special tokens (e.g. <SOT>, <EOT>, <BEG>, etc.)
+        bool print_progress;    // print progress information
+        bool print_realtime;    // print results from within whisper.cpp (avoid it, use callback instead)
+        bool print_timestamps;  // print timestamps for each text segment when printing realtime
+
+        // [EXPERIMENTAL] token-level timestamps
+        bool  token_timestamps; // enable token-level timestamps
+        float thold_pt;         // timestamp token probability threshold (~0.01)
+        float thold_ptsum;      // timestamp token sum probability threshold (~0.01)
+        int   max_len;          // max segment length in characters
+        bool  split_on_word;    // split on word rather than on token (when used with max_len)
+        int   max_tokens;       // max tokens per segment (0 = no limit)
+
+        // [EXPERIMENTAL] speed-up techniques
+        // note: these can significantly reduce the quality of the output
+        bool debug_mode;        // enable debug_mode provides extra info (eg. Dump log_mel)
+        int  audio_ctx;         // overwrite the audio context size (0 = use default)
+
+        // [EXPERIMENTAL] [TDRZ] tinydiarize
+        bool tdrz_enable;       // enable tinydiarize speaker turn detection
+
+        // A regular expression that matches tokens to suppress
+        const char * suppress_regex;
+
+        // tokens to provide to the whisper decoder as initial prompt
+        // these are prepended to any existing text context from a previous call
+        // use whisper_tokenize() to convert text to tokens
+        // maximum of whisper_n_text_ctx()/2 tokens are used (typically 224)
+        const char * initial_prompt;
+        bool carry_initial_prompt; // if true, always prepend initial_prompt to every decode window (may reduce conditioning on previous text)
+        const whisper_token * prompt_tokens;
+        int prompt_n_tokens;
+
+        // for auto-detection, set to nullptr, "" or "auto"
+        const char * language;
+        bool detect_language;
+
+        // common decoding parameters:
+        bool suppress_blank; // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/decoding.py#L89
+        bool suppress_nst;   // non-speech tokens, ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/tokenizer.py#L224-L253
+
+        float temperature;      // initial decoding temperature, ref: https://ai.stackexchange.com/a/32478
+        float max_initial_ts;   // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/decoding.py#L97
+        float length_penalty;   // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L267
+
+        // fallback parameters
+        // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L274-L278
+        float temperature_inc;
+        float entropy_thold;    // similar to OpenAI's "compression_ratio_threshold"
+        float logprob_thold;
+        float no_speech_thold;
+
+        struct {
+            int best_of;    // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L264
+        } greedy;
+
+        struct {
+            int beam_size;  // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L265
+
+            float patience; // TODO: not implemented, ref: https://arxiv.org/pdf/2204.05424.pdf
+        } beam_search;
+
+        // called for every newly generated text segment
+        whisper_new_segment_callback new_segment_callback;
+        void * new_segment_callback_user_data;
+
+        // called on each progress update
+        whisper_progress_callback progress_callback;
+        void * progress_callback_user_data;
+
+        // called each time before the encoder starts
+        whisper_encoder_begin_callback encoder_begin_callback;
+        void * encoder_begin_callback_user_data;
+
+        // called each time before ggml computation starts
+        ggml_abort_callback abort_callback;
+        void * abort_callback_user_data;
+
+        // called by each decoder to filter obtained logits
+        whisper_logits_filter_callback logits_filter_callback;
+        void * logits_filter_callback_user_data;
+
+        const whisper_grammar_element ** grammar_rules;
+        size_t                           n_grammar_rules;
+        size_t                           i_start_rule;
+        float                            grammar_penalty;
+
+        // Voice Activity Detection (VAD) params
+        bool         vad;                         // Enable VAD
+        const char * vad_model_path;              // Path to VAD model
+
+        whisper_vad_params vad_params;
+    };
+
+    // NOTE: this function allocates memory, and it is the responsibility of the caller to free the pointer - see whisper_free_context_params & whisper_free_params()
+    WHISPER_API struct whisper_context_params * whisper_context_default_params_by_ref(void);
+    WHISPER_API struct whisper_context_params   whisper_context_default_params       (void);
+
+    WHISPER_API struct whisper_full_params * whisper_full_default_params_by_ref(enum whisper_sampling_strategy strategy);
+    WHISPER_API struct whisper_full_params   whisper_full_default_params       (enum whisper_sampling_strategy strategy);
+
+    // Run the entire model: PCM -> log mel spectrogram -> encoder -> decoder -> text
+    // Not thread safe for same context
+    // Uses the specified decoding strategy to obtain the text.
+    WHISPER_API int whisper_full(
+                struct whisper_context * ctx,
+            struct whisper_full_params   params,
+                           const float * samples,
+                                   int   n_samples);
+
+    WHISPER_API int whisper_full_with_state(
+                struct whisper_context * ctx,
+                  struct whisper_state * state,
+            struct whisper_full_params   params,
+                           const float * samples,
+                                   int   n_samples);
+
+    // Split the input audio in chunks and process each chunk separately using whisper_full_with_state()
+    // Result is stored in the default state of the context
+    // Not thread safe if executed in parallel on the same context.
+    // It seems this approach can offer some speedup in some cases.
+    // However, the transcription accuracy can be worse at the beginning and end of each chunk.
+    WHISPER_API int whisper_full_parallel(
+                struct whisper_context * ctx,
+            struct whisper_full_params   params,
+                           const float * samples,
+                                   int   n_samples,
+                                   int   n_processors);
+
+    // Number of generated text segments
+    // A segment can be a few words, a sentence, or even a paragraph.
+    WHISPER_API int whisper_full_n_segments           (struct whisper_context * ctx);
+    WHISPER_API int whisper_full_n_segments_from_state(struct whisper_state * state);
+
+    // Language id associated with the context's default state
+    WHISPER_API int whisper_full_lang_id(struct whisper_context * ctx);
+
+    // Language id associated with the provided state
+    WHISPER_API int whisper_full_lang_id_from_state(struct whisper_state * state);
+
+    // Get the start and end time of the specified segment
+    WHISPER_API int64_t whisper_full_get_segment_t0           (struct whisper_context * ctx, int i_segment);
+    WHISPER_API int64_t whisper_full_get_segment_t0_from_state(struct whisper_state * state, int i_segment);
+
+    WHISPER_API int64_t whisper_full_get_segment_t1           (struct whisper_context * ctx, int i_segment);
+    WHISPER_API int64_t whisper_full_get_segment_t1_from_state(struct whisper_state * state, int i_segment);
+
+    // Get whether the next segment is predicted as a speaker turn
+    WHISPER_API bool whisper_full_get_segment_speaker_turn_next(struct whisper_context * ctx, int i_segment);
+    WHISPER_API bool whisper_full_get_segment_speaker_turn_next_from_state(struct whisper_state * state, int i_segment);
+
+    // Get the text of the specified segment
+    WHISPER_API const char * whisper_full_get_segment_text           (struct whisper_context * ctx, int i_segment);
+    WHISPER_API const char * whisper_full_get_segment_text_from_state(struct whisper_state * state, int i_segment);
+
+    // Get number of tokens in the specified segment
+    WHISPER_API int whisper_full_n_tokens           (struct whisper_context * ctx, int i_segment);
+    WHISPER_API int whisper_full_n_tokens_from_state(struct whisper_state * state, int i_segment);
+
+    // Get the token text of the specified token in the specified segment
+    WHISPER_API const char * whisper_full_get_token_text           (struct whisper_context * ctx, int i_segment, int i_token);
+    WHISPER_API const char * whisper_full_get_token_text_from_state(struct whisper_context * ctx, struct whisper_state * state, int i_segment, int i_token);
+
+    WHISPER_API whisper_token whisper_full_get_token_id           (struct whisper_context * ctx, int i_segment, int i_token);
+    WHISPER_API whisper_token whisper_full_get_token_id_from_state(struct whisper_state * state, int i_segment, int i_token);
+
+    // Get token data for the specified token in the specified segment
+    // This contains probabilities, timestamps, etc.
+    WHISPER_API whisper_token_data whisper_full_get_token_data           (struct whisper_context * ctx, int i_segment, int i_token);
+    WHISPER_API whisper_token_data whisper_full_get_token_data_from_state(struct whisper_state * state, int i_segment, int i_token);
+
+    // Get the probability of the specified token in the specified segment
+    WHISPER_API float whisper_full_get_token_p           (struct whisper_context * ctx, int i_segment, int i_token);
+    WHISPER_API float whisper_full_get_token_p_from_state(struct whisper_state * state, int i_segment, int i_token);
+
+    //
+    // Voice Activity Detection (VAD)
+    //
+
+    struct whisper_vad_context;
+
+    WHISPER_API struct whisper_vad_params whisper_vad_default_params(void);
+
+    struct whisper_vad_context_params {
+        int   n_threads;  // The number of threads to use for processing.
+        bool  use_gpu;
+        int   gpu_device; // CUDA device
+    };
+
+    WHISPER_API struct whisper_vad_context_params whisper_vad_default_context_params(void);
+
+    WHISPER_API struct whisper_vad_context * whisper_vad_init_from_file_with_params(const char * path_model,              struct whisper_vad_context_params params);
+    WHISPER_API struct whisper_vad_context * whisper_vad_init_with_params          (struct whisper_model_loader * loader, struct whisper_vad_context_params params);
+
+    WHISPER_API bool whisper_vad_detect_speech(
+            struct whisper_vad_context * vctx,
+                           const float * samples,
+                                   int   n_samples);
+
+    WHISPER_API int     whisper_vad_n_probs(struct whisper_vad_context * vctx);
+    WHISPER_API float * whisper_vad_probs  (struct whisper_vad_context * vctx);
+
+    struct whisper_vad_segments;
+
+    WHISPER_API struct whisper_vad_segments * whisper_vad_segments_from_probs(
+            struct whisper_vad_context * vctx,
+            struct whisper_vad_params    params);
+
+    WHISPER_API struct whisper_vad_segments * whisper_vad_segments_from_samples(
+            struct whisper_vad_context * vctx,
+            struct whisper_vad_params    params,
+                           const float * samples,
+                                   int   n_samples);
+
+    WHISPER_API int whisper_vad_segments_n_segments(struct whisper_vad_segments * segments);
+
+    WHISPER_API float whisper_vad_segments_get_segment_t0(struct whisper_vad_segments * segments, int i_segment);
+    WHISPER_API float whisper_vad_segments_get_segment_t1(struct whisper_vad_segments * segments, int i_segment);
+
+    WHISPER_API void whisper_vad_free_segments(struct whisper_vad_segments * segments);
+    WHISPER_API void whisper_vad_free         (struct whisper_vad_context  * ctx);
+
+    ////////////////////////////////////////////////////////////////////////////
+
+    // Temporary helpers needed for exposing ggml interface
+
+    WHISPER_API int          whisper_bench_memcpy          (int n_threads);
+    WHISPER_API const char * whisper_bench_memcpy_str      (int n_threads);
+    WHISPER_API int          whisper_bench_ggml_mul_mat    (int n_threads);
+    WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads);
+
+    // Control logging output; default behavior is to print to stderr
+
+    WHISPER_API void whisper_log_set(ggml_log_callback log_callback, void * user_data);
+
+    // Get the no_speech probability for the specified segment
+    WHISPER_API float whisper_full_get_segment_no_speech_prob           (struct whisper_context * ctx, int i_segment);
+    WHISPER_API float whisper_full_get_segment_no_speech_prob_from_state(struct whisper_state * state, int i_segment);
+#ifdef __cplusplus
+}
+#endif
+
+#endif
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diff --git a/ggml/whispercpp/whispercpp.go b/ggml/whispercpp/whispercpp.go
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--- /dev/null
+++ b/ggml/whispercpp/whispercpp.go
@@ -0,0 +1,160 @@
+//go:build whispercpp
+
+// Copyright 2025 FootprintAI
+// SPDX-License-Identifier: Apache-2.0
+
+// Package whispercpp provides Go bindings for whisper.cpp via CGO.
+// Build with -tags whispercpp to enable; without the tag, stub implementations are used.
+package whispercpp
+
+/*
+#cgo CFLAGS: -I${SRCDIR}/third_party/include -I${SRCDIR}/third_party/ggml/include
+#cgo CXXFLAGS: -std=c++17 -I${SRCDIR}/third_party/include -I${SRCDIR}/third_party/ggml/include
+#cgo darwin,arm64 LDFLAGS: -L${SRCDIR}/third_party/prebuilt/darwin-arm64
+#cgo darwin,amd64 LDFLAGS: -L${SRCDIR}/third_party/prebuilt/darwin-amd64
+#cgo linux,amd64 LDFLAGS: -L${SRCDIR}/third_party/prebuilt/linux-amd64
+#cgo LDFLAGS: -lwhisper -lcommon -lggml-cpu -lggml-base -lggml -lstdc++ -lm
+#cgo darwin LDFLAGS: -framework Accelerate -framework Metal -framework Foundation
+#include <stdlib.h>
+#include "whisper.h"
+*/
+import "C"
+
+import (
+	"errors"
+	"fmt"
+	"sync"
+	"time"
+	"unsafe"
+)
+
+// Segment represents a transcribed audio segment with timing information.
+type Segment struct {
+	Start time.Duration
+	End   time.Duration
+	Text  string
+}
+
+// Model wraps a loaded whisper.cpp model context.
+type Model struct {
+	c  *C.struct_whisper_context
+	mu sync.Mutex
+}
+
+// LoadModel loads a whisper GGML model from path.
+func LoadModel(path string, opts ...ModelOption) (*Model, error) {
+	cfg := defaultModelConfig()
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	params := C.whisper_context_default_params()
+	params.use_gpu = C.bool(cfg.useGPU)
+	params.flash_attn = C.bool(cfg.flashAttn)
+
+	cpath := C.CString(path)
+	defer C.free(unsafe.Pointer(cpath))
+
+	ctx := C.whisper_init_from_file_with_params(cpath, params)
+	if ctx == nil {
+		return nil, fmt.Errorf("failed to load whisper model: %s", path)
+	}
+	return &Model{c: ctx}, nil
+}
+
+// Close frees the model resources.
+func (m *Model) Close() {
+	m.mu.Lock()
+	defer m.mu.Unlock()
+	if m.c != nil {
+		C.whisper_free(m.c)
+		m.c = nil
+	}
+}
+
+// IsMultilingual returns true if the model supports multiple languages.
+func (m *Model) IsMultilingual() bool {
+	m.mu.Lock()
+	defer m.mu.Unlock()
+	if m.c == nil {
+		return false
+	}
+	return C.whisper_is_multilingual(m.c) != 0
+}
+
+// Transcribe runs the full whisper pipeline on PCM audio data.
+// pcmData must be 16kHz mono float32 samples.
+func (m *Model) Transcribe(pcmData []float32, opts ...TranscribeOption) ([]Segment, error) {
+	m.mu.Lock()
+	defer m.mu.Unlock()
+
+	if m.c == nil {
+		return nil, errors.New("model is closed")
+	}
+	if len(pcmData) == 0 {
+		return nil, errors.New("empty audio data")
+	}
+
+	cfg := defaultTranscribeConfig()
+	for _, o := range opts {
+		o(&cfg)
+	}
+
+	params := C.whisper_full_default_params(C.WHISPER_SAMPLING_GREEDY)
+	params.n_threads = C.int(cfg.threads)
+	params.translate = C.bool(cfg.translate)
+	params.no_timestamps = C.bool(!cfg.timestamps)
+	params.single_segment = C.bool(cfg.singleSegment)
+	params.print_special = C.bool(false)
+	params.print_progress = C.bool(false)
+	params.print_realtime = C.bool(false)
+	params.print_timestamps = C.bool(false)
+	params.token_timestamps = C.bool(cfg.tokenTimestamps)
+	params.temperature = C.float(cfg.temperature)
+	params.max_tokens = C.int(cfg.maxTokens)
+
+	if cfg.language != "" {
+		clang := C.CString(cfg.language)
+		defer C.free(unsafe.Pointer(clang))
+		params.language = clang
+	}
+
+	if cfg.prompt != "" {
+		cprompt := C.CString(cfg.prompt)
+		defer C.free(unsafe.Pointer(cprompt))
+		params.initial_prompt = cprompt
+	}
+
+	rc := C.whisper_full(
+		m.c,
+		params,
+		(*C.float)(unsafe.Pointer(&pcmData[0])),
+		C.int(len(pcmData)),
+	)
+	if rc != 0 {
+		return nil, fmt.Errorf("whisper_full failed with code %d", rc)
+	}
+
+	nSegments := int(C.whisper_full_n_segments(m.c))
+	segments := make([]Segment, 0, nSegments)
+	for i := 0; i < nSegments; i++ {
+		t0 := int64(C.whisper_full_get_segment_t0(m.c, C.int(i)))
+		t1 := int64(C.whisper_full_get_segment_t1(m.c, C.int(i)))
+		text := C.GoString(C.whisper_full_get_segment_text(m.c, C.int(i)))
+		segments = append(segments, Segment{
+			Start: time.Duration(t0) * 10 * time.Millisecond,
+			End:   time.Duration(t1) * 10 * time.Millisecond,
+			Text:  text,
+		})
+	}
+
+	return segments, nil
+}
+
+// LangID returns the language ID for the given language string.
+// Returns -1 if not found.
+func LangID(lang string) int {
+	clang := C.CString(lang)
+	defer C.free(unsafe.Pointer(clang))
+	return int(C.whisper_lang_id(clang))
+}
diff --git a/ggml/whispercpp/whispercpp_stub.go b/ggml/whispercpp/whispercpp_stub.go
new file mode 100644
index 0000000..740863a
--- /dev/null
+++ b/ggml/whispercpp/whispercpp_stub.go
@@ -0,0 +1,65 @@
+//go:build !whispercpp
+
+// Copyright 2025 FootprintAI
+// SPDX-License-Identifier: Apache-2.0
+
+// Package whispercpp provides stub implementations when whisper.cpp is not available.
+// Build with -tags whispercpp to enable the real implementation.
+package whispercpp
+
+import (
+	"errors"
+	"time"
+)
+
+var errNotAvailable = errors.New("whispercpp: not available, build with -tags whispercpp")
+
+// Segment represents a transcribed audio segment with timing information.
+type Segment struct {
+	Start time.Duration
+	End   time.Duration
+	Text  string
+}
+
+// Model is a stub.
+type Model struct{}
+
+// LoadModel returns an error without the whispercpp build tag.
+func LoadModel(path string, opts ...ModelOption) (*Model, error) {
+	return nil, errNotAvailable
+}
+
+// Close is a no-op.
+func (m *Model) Close() {}
+
+// IsMultilingual returns false.
+func (m *Model) IsMultilingual() bool { return false }
+
+// Transcribe returns an error without the whispercpp build tag.
+func (m *Model) Transcribe(pcmData []float32, opts ...TranscribeOption) ([]Segment, error) {
+	return nil, errNotAvailable
+}
+
+// LangID returns -1.
+func LangID(lang string) int { return -1 }
+
+// ModelOption configures model loading (stub).
+type ModelOption func(*modelConfig)
+type modelConfig struct{}
+
+func WithGPU(enabled bool) ModelOption            { return func(*modelConfig) {} }
+func WithFlashAttention(enabled bool) ModelOption { return func(*modelConfig) {} }
+
+// TranscribeOption configures transcription (stub).
+type TranscribeOption func(*transcribeConfig)
+type transcribeConfig struct{}
+
+func WithThreads(n int) TranscribeOption             { return func(*transcribeConfig) {} }
+func WithLanguage(lang string) TranscribeOption       { return func(*transcribeConfig) {} }
+func WithTranslate(enabled bool) TranscribeOption     { return func(*transcribeConfig) {} }
+func WithTimestamps(enabled bool) TranscribeOption    { return func(*transcribeConfig) {} }
+func WithTokenTimestamps(enabled bool) TranscribeOption { return func(*transcribeConfig) {} }
+func WithSingleSegment(enabled bool) TranscribeOption { return func(*transcribeConfig) {} }
+func WithTemperature(t float32) TranscribeOption      { return func(*transcribeConfig) {} }
+func WithMaxTokens(n int) TranscribeOption            { return func(*transcribeConfig) {} }
+func WithPrompt(prompt string) TranscribeOption       { return func(*transcribeConfig) {} }
diff --git a/ggml/whispercpp/whispercpp_stub_test.go b/ggml/whispercpp/whispercpp_stub_test.go
new file mode 100644
index 0000000..68d6703
--- /dev/null
+++ b/ggml/whispercpp/whispercpp_stub_test.go
@@ -0,0 +1,57 @@
+//go:build !whispercpp
+
+// Copyright 2025 FootprintAI
+// SPDX-License-Identifier: Apache-2.0
+
+package whispercpp
+
+import (
+	"testing"
+)
+
+func TestStubLoadModel(t *testing.T) {
+	_, err := LoadModel("/nonexistent.bin")
+	if err == nil {
+		t.Fatal("expected error from stub LoadModel")
+	}
+	if err.Error() != "whispercpp: not available, build with -tags whispercpp" {
+		t.Fatalf("unexpected error: %v", err)
+	}
+}
+
+func TestStubTranscribe(t *testing.T) {
+	var m Model
+
+	_, err := m.Transcribe([]float32{0.1, 0.2, 0.3})
+	if err == nil {
+		t.Fatal("expected error from stub Transcribe")
+	}
+
+	if m.IsMultilingual() {
+		t.Fatal("expected false from stub IsMultilingual")
+	}
+
+	// Close should be a no-op
+	m.Close()
+}
+
+func TestStubLangID(t *testing.T) {
+	if LangID("en") != -1 {
+		t.Fatal("expected -1 from stub LangID")
+	}
+}
+
+func TestStubOptions(t *testing.T) {
+	// Options should be constructable without error
+	_ = WithGPU(true)
+	_ = WithFlashAttention(false)
+	_ = WithThreads(4)
+	_ = WithLanguage("en")
+	_ = WithTranslate(true)
+	_ = WithTimestamps(true)
+	_ = WithTokenTimestamps(false)
+	_ = WithSingleSegment(false)
+	_ = WithTemperature(0.0)
+	_ = WithMaxTokens(100)
+	_ = WithPrompt("test")
+}