From 27e1cb3ad6eb5f53acdf815ed4328be34e92309c Mon Sep 17 00:00:00 2001
From: jlarson4 <jonahalarson@comcast.net>
Date: Wed, 8 Apr 2026 11:08:27 -0500
Subject: [PATCH 1/2] Initial DeepSeek setup

---
 .../model_bridge/test_deepseek_adapter.py     | 168 +++++++++++
 .../test_mla_attention_bridge.py              | 238 ++++++++++++++++
 .../factories/architecture_adapter_factory.py |   2 +
 .../model_bridge/component_setup.py           |  21 +-
 .../generalized_components/__init__.py        |   4 +
 .../generalized_components/mla_attention.py   | 260 ++++++++++++++++++
 .../supported_architectures/__init__.py       |   4 +
 .../supported_architectures/deepseek_v3.py    | 128 +++++++++
 8 files changed, 822 insertions(+), 3 deletions(-)
 create mode 100644 tests/integration/model_bridge/test_deepseek_adapter.py
 create mode 100644 tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py
 create mode 100644 transformer_lens/model_bridge/generalized_components/mla_attention.py
 create mode 100644 transformer_lens/model_bridge/supported_architectures/deepseek_v3.py

diff --git a/tests/integration/model_bridge/test_deepseek_adapter.py b/tests/integration/model_bridge/test_deepseek_adapter.py
new file mode 100644
index 000000000..002a839f9
--- /dev/null
+++ b/tests/integration/model_bridge/test_deepseek_adapter.py
@@ -0,0 +1,168 @@
+"""Integration tests for DeepSeek V3 architecture adapter.
+
+Uses a tiny programmatic DeepseekV3 model (~6.5M params) saved to a temp dir,
+since no small pretrained DeepSeek V3/R1 models exist (smallest is 671B).
+"""
+
+import tempfile
+
+import pytest
+import torch
+from transformers import AutoTokenizer, DeepseekV3Config, DeepseekV3ForCausalLM
+
+from transformer_lens.model_bridge.bridge import TransformerBridge
+
+
+@pytest.fixture(scope="module")
+def tiny_deepseek_bridge():
+    """Create a TransformerBridge wrapping a tiny programmatic DeepSeek V3 model."""
+    tiny_config = DeepseekV3Config(
+        hidden_size=256,
+        intermediate_size=512,
+        num_hidden_layers=4,  # 1 dense + 3 MoE
+        num_attention_heads=8,
+        q_lora_rank=64,
+        kv_lora_rank=32,
+        qk_nope_head_dim=16,
+        qk_rope_head_dim=8,
+        v_head_dim=16,
+        vocab_size=1000,
+        first_k_dense_replace=1,
+        n_routed_experts=8,
+        n_shared_experts=1,
+        num_experts_per_tok=2,
+        n_group=2,
+        topk_group=1,
+        max_position_embeddings=128,
+        moe_intermediate_size=256,
+    )
+    hf_model = DeepseekV3ForCausalLM(tiny_config)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        hf_model.save_pretrained(tmpdir)
+        tok = AutoTokenizer.from_pretrained("gpt2")
+        tok.save_pretrained(tmpdir)
+        bridge = TransformerBridge.boot_transformers(tmpdir, device="cpu")
+        yield bridge
+
+
+class TestDeepSeekBridgeCreation:
+    """Test bridge creation and structural validation."""
+
+    def test_bridge_has_correct_block_count(self, tiny_deepseek_bridge):
+        assert len(tiny_deepseek_bridge.blocks) == 4
+
+    def test_bridge_has_embed_and_unembed(self, tiny_deepseek_bridge):
+        assert hasattr(tiny_deepseek_bridge, "embed")
+        assert hasattr(tiny_deepseek_bridge, "unembed")
+        assert hasattr(tiny_deepseek_bridge, "ln_final")
+
+    def test_attention_is_mla(self, tiny_deepseek_bridge):
+        from transformer_lens.model_bridge.generalized_components.mla_attention import (
+            MLAAttentionBridge,
+        )
+
+        attn = tiny_deepseek_bridge.blocks[0].attn
+        assert isinstance(attn, MLAAttentionBridge)
+
+
+class TestDeepSeekForwardPass:
+    """Test forward pass produces valid output."""
+
+    def test_forward_returns_logits(self, tiny_deepseek_bridge):
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        with torch.no_grad():
+            output = tiny_deepseek_bridge(tokens)
+        assert output.shape == (1, 4, 1000)
+        assert not torch.isnan(output).any()
+        assert not torch.isinf(output).any()
+
+    def test_forward_matches_hf(self, tiny_deepseek_bridge):
+        """Bridge output should be close to HF model output."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        hf_model = tiny_deepseek_bridge.original_model
+        with torch.no_grad():
+            bridge_out = tiny_deepseek_bridge(tokens)
+            hf_out = hf_model(tokens).logits
+        # SDPA vs eager difference expected
+        max_diff = (bridge_out - hf_out).abs().max().item()
+        assert max_diff < 0.2, f"Bridge vs HF max diff = {max_diff}"
+
+
+class TestDeepSeekDenseVsMoELayers:
+    """Test that dense and MoE layers are handled correctly."""
+
+    def test_dense_layer_has_no_moe_hooks(self, tiny_deepseek_bridge):
+        """Dense layer (blocks[0].mlp) should NOT have gate/shared_experts hooks."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
+        cache_keys = set(cache.keys())
+
+        assert not any(
+            "blocks.0.mlp.gate" in k for k in cache_keys
+        ), "Dense layer should not have gate hooks"
+        assert not any(
+            "blocks.0.mlp.shared_experts" in k for k in cache_keys
+        ), "Dense layer should not have shared_experts hooks"
+
+    def test_moe_layer_has_gate_hooks(self, tiny_deepseek_bridge):
+        """MoE layer (blocks[1].mlp) SHOULD have gate hooks."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
+        cache_keys = set(cache.keys())
+
+        assert any("blocks.1.mlp.gate" in k for k in cache_keys), "MoE layer should have gate hooks"
+
+    def test_moe_layer_has_shared_experts_hooks(self, tiny_deepseek_bridge):
+        """MoE layer should have shared_experts hooks."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
+        cache_keys = set(cache.keys())
+
+        assert any(
+            "blocks.1.mlp.shared_experts" in k for k in cache_keys
+        ), "MoE layer should have shared_experts hooks"
+
+    def test_both_layers_have_mlp_hooks(self, tiny_deepseek_bridge):
+        """Both dense and MoE layers should have basic hook_in/hook_out."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
+        cache_keys = set(cache.keys())
+
+        assert "blocks.0.mlp.hook_in" in cache_keys
+        assert "blocks.0.mlp.hook_out" in cache_keys
+        assert "blocks.1.mlp.hook_in" in cache_keys
+        assert "blocks.1.mlp.hook_out" in cache_keys
+
+    def test_both_layers_produce_non_nan(self, tiny_deepseek_bridge):
+        """Both dense and MoE layers should produce non-NaN output."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
+
+        for i in [0, 1]:
+            key = f"blocks.{i}.mlp.hook_out"
+            assert key in cache, f"Missing {key}"
+            assert not torch.isnan(cache[key]).any(), f"NaN in {key}"
+
+
+class TestDeepSeekAttentionHooks:
+    """Test MLA attention hooks fire on all layers."""
+
+    def test_attention_hooks_fire_all_layers(self, tiny_deepseek_bridge):
+        """Attention hooks should fire on every layer."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
+        cache_keys = set(cache.keys())
+
+        for i in range(4):
+            assert f"blocks.{i}.attn.hook_in" in cache_keys, f"Layer {i} missing attn.hook_in"
+            assert f"blocks.{i}.attn.hook_out" in cache_keys, f"Layer {i} missing attn.hook_out"
+
+    def test_mla_latent_hooks_fire(self, tiny_deepseek_bridge):
+        """MLA-specific latent hooks should fire."""
+        tokens = torch.tensor([[1, 2, 3, 4]])
+        _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
+        cache_keys = set(cache.keys())
+
+        assert any("hook_q_latent" in k for k in cache_keys), "hook_q_latent should fire"
+        assert any("hook_kv_latent" in k for k in cache_keys), "hook_kv_latent should fire"
diff --git a/tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py b/tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py
new file mode 100644
index 000000000..b41415797
--- /dev/null
+++ b/tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py
@@ -0,0 +1,238 @@
+"""Unit tests for MLAAttentionBridge (DeepSeek Multi-Head Latent Attention).
+
+Uses a tiny programmatic DeepseekV3 model (~6.5M params) since no small
+pretrained DeepSeek V3/R1 models exist.
+"""
+
+import pytest
+import torch
+from transformers import DeepseekV3Config, DeepseekV3ForCausalLM
+
+from transformer_lens.model_bridge.generalized_components.mla_attention import (
+    MLAAttentionBridge,
+)
+
+
+@pytest.fixture(scope="module")
+def tiny_config():
+    return DeepseekV3Config(
+        hidden_size=256,
+        intermediate_size=512,
+        num_hidden_layers=4,
+        num_attention_heads=8,
+        q_lora_rank=64,
+        kv_lora_rank=32,
+        qk_nope_head_dim=16,
+        qk_rope_head_dim=8,
+        v_head_dim=16,
+        vocab_size=1000,
+        first_k_dense_replace=1,
+        n_routed_experts=8,
+        n_shared_experts=1,
+        num_experts_per_tok=2,
+        n_group=2,
+        topk_group=1,
+        max_position_embeddings=128,
+        moe_intermediate_size=256,
+    )
+
+
+@pytest.fixture(scope="module")
+def tiny_model(tiny_config):
+    return DeepseekV3ForCausalLM(tiny_config)
+
+
+@pytest.fixture(scope="module")
+def hf_attn(tiny_model):
+    """The raw HF attention module from layer 0."""
+    return tiny_model.model.layers[0].self_attn
+
+
+@pytest.fixture(scope="module")
+def mla_bridge(tiny_config, hf_attn, tiny_model):
+    """An MLAAttentionBridge wrapping layer 0's attention."""
+    bridge = MLAAttentionBridge(
+        name="self_attn",
+        config=tiny_config,
+        submodules={},
+    )
+    bridge.set_original_component(hf_attn)
+    bridge.set_rotary_emb(tiny_model.model.rotary_emb)
+    return bridge
+
+
+class TestMLAAttentionBridgeHooks:
+    """Test hook registration and firing."""
+
+    def test_all_expected_hooks_exist(self, mla_bridge):
+        """All MLA-specific hooks should be registered."""
+        expected = [
+            "hook_in",
+            "hook_out",
+            "hook_q_latent",
+            "hook_kv_latent",
+            "hook_q",
+            "hook_k",
+            "hook_v",
+            "hook_rot_q",
+            "hook_rot_k",
+            "hook_attn_scores",
+            "hook_pattern",
+            "hook_cos",
+            "hook_sin",
+        ]
+        for hook_name in expected:
+            assert hasattr(mla_bridge, hook_name), f"Missing hook: {hook_name}"
+
+    def test_W_Q_raises_not_implemented(self, mla_bridge):
+        """Accessing W_Q on MLA should raise NotImplementedError."""
+        with pytest.raises(NotImplementedError, match="not available on MLA"):
+            _ = mla_bridge.W_Q
+
+    def test_W_K_raises_not_implemented(self, mla_bridge):
+        """Accessing W_K on MLA should raise NotImplementedError."""
+        with pytest.raises(NotImplementedError, match="not available on MLA"):
+            _ = mla_bridge.W_K
+
+
+class TestMLAAttentionBridgeForward:
+    """Test forward pass correctness."""
+
+    @pytest.fixture
+    def sample_inputs(self, tiny_config, tiny_model):
+        """Create sample inputs for attention forward."""
+        batch, seq = 2, 8
+        hidden_states = torch.randn(batch, seq, tiny_config.hidden_size)
+        # Get position embeddings from the model's rotary_emb
+        position_ids = torch.arange(seq).unsqueeze(0).expand(batch, -1)
+        cos, sin = tiny_model.model.rotary_emb(hidden_states, position_ids)
+        return hidden_states, (cos, sin)
+
+    def test_output_matches_hf(self, mla_bridge, hf_attn, sample_inputs, tiny_model):
+        """Bridge forward should produce output close to HF attention.
+
+        Note: HF defaults to SDPA while the bridge reimplements with manual matmul
+        (eager-style). HF's eager attention crashes on tiny MLA configs due to
+        head count mismatches in GQA expansion. SDPA vs eager produces small
+        numerical differences (~0.01 mean, ~0.09 max in float32) which is expected.
+        """
+        hidden_states, position_embeddings = sample_inputs
+
+        with torch.no_grad():
+            # HF native forward (uses SDPA by default)
+            hf_output = hf_attn(
+                hidden_states,
+                position_embeddings=position_embeddings,
+                attention_mask=None,
+            )
+            hf_attn_out = hf_output[0]
+
+            # Bridge forward (uses manual matmul)
+            bridge_output = mla_bridge(
+                hidden_states,
+                position_embeddings=position_embeddings,
+                attention_mask=None,
+            )
+            bridge_attn_out = bridge_output[0]
+
+        # SDPA vs eager produces small numerical differences in float32
+        max_diff = (hf_attn_out - bridge_attn_out).abs().max().item()
+        mean_diff = (hf_attn_out - bridge_attn_out).abs().mean().item()
+        assert max_diff < 0.2, f"Output too different: max diff = {max_diff}"
+        assert mean_diff < 0.02, f"Output too different: mean diff = {mean_diff}"
+
+    def test_hooks_fire_and_have_correct_shapes(self, mla_bridge, sample_inputs, tiny_config):
+        """All hooks should fire and produce tensors with expected shapes.
+
+        Uses PyTorch forward hooks directly since MLAAttentionBridge is a
+        GeneralizedComponent (nn.Module), not a HookedRootModule with run_with_hooks.
+        """
+        hidden_states, position_embeddings = sample_inputs
+        batch, seq = hidden_states.shape[:2]
+        captured = {}
+
+        hooks_to_check = [
+            "hook_q_latent",
+            "hook_kv_latent",
+            "hook_q",
+            "hook_k",
+            "hook_v",
+            "hook_rot_q",
+            "hook_rot_k",
+            "hook_attn_scores",
+            "hook_pattern",
+        ]
+
+        handles = []
+        for name in hooks_to_check:
+            hook_point = getattr(mla_bridge, name)
+
+            def make_capture(n):
+                def hook_fn(module, input, output):
+                    captured[n] = output.shape
+
+                return hook_fn
+
+            handles.append(hook_point.register_forward_hook(make_capture(name)))
+
+        try:
+            with torch.no_grad():
+                mla_bridge(
+                    hidden_states,
+                    position_embeddings=position_embeddings,
+                    attention_mask=None,
+                )
+        finally:
+            for h in handles:
+                h.remove()
+
+        n_heads = tiny_config.num_attention_heads
+        qk_head_dim = tiny_config.qk_nope_head_dim + tiny_config.qk_rope_head_dim
+
+        # Verify all hooks fired
+        for name in hooks_to_check:
+            assert name in captured, f"Hook {name} did not fire"
+
+        # Verify shapes
+        assert captured["hook_q_latent"] == (batch, seq, tiny_config.q_lora_rank)
+        assert captured["hook_kv_latent"] == (batch, seq, tiny_config.kv_lora_rank)
+        assert captured["hook_q"] == (batch, n_heads, seq, qk_head_dim)
+        assert captured["hook_k"] == (batch, n_heads, seq, qk_head_dim)
+        assert captured["hook_v"] == (batch, n_heads, seq, tiny_config.v_head_dim)
+        assert captured["hook_attn_scores"] == (batch, n_heads, seq, seq)
+        assert captured["hook_pattern"] == (batch, n_heads, seq, seq)
+
+    def test_hook_q_is_post_rope(self, mla_bridge, sample_inputs):
+        """hook_q should capture the final Q (after RoPE concat, not pre-RoPE)."""
+        hidden_states, position_embeddings = sample_inputs
+        q_values: list[torch.Tensor] = []
+        rot_q_values: list[torch.Tensor] = []
+
+        def capture_q(module, input, output):
+            q_values.append(output.clone())
+
+        def capture_rot_q(module, input, output):
+            rot_q_values.append(output.clone())
+
+        h1 = mla_bridge.hook_q.register_forward_hook(capture_q)
+        h2 = mla_bridge.hook_rot_q.register_forward_hook(capture_rot_q)
+
+        try:
+            with torch.no_grad():
+                mla_bridge(
+                    hidden_states,
+                    position_embeddings=position_embeddings,
+                    attention_mask=None,
+                )
+        finally:
+            h1.remove()
+            h2.remove()
+
+        q = q_values[0]
+        rot_q = rot_q_values[0]
+        # The rope portion of Q (last qk_rope_head_dim dims) should match hook_rot_q
+        qk_rope_dim = mla_bridge._qk_rope_head_dim
+        q_rope_portion = q[..., -qk_rope_dim:]
+        assert torch.allclose(
+            q_rope_portion, rot_q, atol=1e-5
+        ), "hook_q rope portion should match hook_rot_q"
diff --git a/transformer_lens/factories/architecture_adapter_factory.py b/transformer_lens/factories/architecture_adapter_factory.py
index 37fd62bbd..1c5e60436 100644
--- a/transformer_lens/factories/architecture_adapter_factory.py
+++ b/transformer_lens/factories/architecture_adapter_factory.py
@@ -9,6 +9,7 @@
     ApertusArchitectureAdapter,
     BertArchitectureAdapter,
     BloomArchitectureAdapter,
+    DeepSeekV3ArchitectureAdapter,
     Gemma1ArchitectureAdapter,
     Gemma2ArchitectureAdapter,
     Gemma3ArchitectureAdapter,
@@ -52,6 +53,7 @@
     "ApertusForCausalLM": ApertusArchitectureAdapter,
     "BertForMaskedLM": BertArchitectureAdapter,
     "BloomForCausalLM": BloomArchitectureAdapter,
+    "DeepseekV3ForCausalLM": DeepSeekV3ArchitectureAdapter,
     "GemmaForCausalLM": Gemma1ArchitectureAdapter,  # Default to Gemma1 as it's the original version
     "Gemma1ForCausalLM": Gemma1ArchitectureAdapter,
     "Gemma2ForCausalLM": Gemma2ArchitectureAdapter,
diff --git a/transformer_lens/model_bridge/component_setup.py b/transformer_lens/model_bridge/component_setup.py
index d32f787df..391724e5d 100644
--- a/transformer_lens/model_bridge/component_setup.py
+++ b/transformer_lens/model_bridge/component_setup.py
@@ -2,8 +2,11 @@
 
 "Component setup utilities for creating and configuring bridged components."
 import copy
+import logging
 from typing import TYPE_CHECKING, Any, cast
 
+logger = logging.getLogger(__name__)
+
 import torch.nn as nn
 
 from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter
@@ -95,9 +98,21 @@ def setup_submodules(
                     original_subcomponent = original_model
                 else:
                     remote_path = submodule.name
-                    original_subcomponent = architecture_adapter.get_remote_component(
-                        original_model, remote_path
-                    )
+                    try:
+                        original_subcomponent = architecture_adapter.get_remote_component(
+                            original_model, remote_path
+                        )
+                    except AttributeError:
+                        # Graceful skip: the HF component doesn't have this submodule.
+                        # This is expected for architectures with per-layer variation
+                        # (e.g., DeepSeek's dense vs MoE layers where dense layers lack
+                        # gate/shared_experts submodules).
+                        logger.debug(
+                            "Skipping submodule '%s' — not found on %s",
+                            module_name,
+                            original_model.__class__.__name__,
+                        )
+                        continue
                 submodule.set_original_component(original_subcomponent)
                 setup_submodules(submodule, architecture_adapter, original_subcomponent)
                 if submodule.name is not None:
diff --git a/transformer_lens/model_bridge/generalized_components/__init__.py b/transformer_lens/model_bridge/generalized_components/__init__.py
index ca38829c0..539816994 100644
--- a/transformer_lens/model_bridge/generalized_components/__init__.py
+++ b/transformer_lens/model_bridge/generalized_components/__init__.py
@@ -39,6 +39,9 @@
     JointQKVPositionEmbeddingsAttentionBridge,
 )
 from transformer_lens.model_bridge.generalized_components.linear import LinearBridge
+from transformer_lens.model_bridge.generalized_components.mla_attention import (
+    MLAAttentionBridge,
+)
 from transformer_lens.model_bridge.generalized_components.mlp import MLPBridge
 from transformer_lens.model_bridge.generalized_components.moe import MoEBridge
 from transformer_lens.model_bridge.generalized_components.normalization import (
@@ -91,6 +94,7 @@
     "LinearBridge",
     "MLPBridge",
     "GatedMLPBridge",
+    "MLAAttentionBridge",
     "MoEBridge",
     "PositionEmbeddingsAttentionBridge",
     "SymbolicBridge",
diff --git a/transformer_lens/model_bridge/generalized_components/mla_attention.py b/transformer_lens/model_bridge/generalized_components/mla_attention.py
new file mode 100644
index 000000000..bf5822be4
--- /dev/null
+++ b/transformer_lens/model_bridge/generalized_components/mla_attention.py
@@ -0,0 +1,260 @@
+"""Multi-Head Latent Attention (MLA) bridge component for DeepSeek models.
+
+MLA compresses Q and KV into lower-dimensional latent spaces via LoRA-style
+projections before standard attention. This component reimplements the MLA
+forward path step-by-step with hooks at each meaningful stage, exposing:
+
+- hook_q_latent / hook_kv_latent: compressed representations (the information bottleneck)
+- hook_q / hook_k / hook_v: final Q/K/V entering attention (post-decompression, post-RoPE)
+- hook_rot_q / hook_rot_k: after RoPE on the rope portion splits
+- hook_attn_scores / hook_pattern: pre/post-softmax attention weights
+- hook_z: pre-output-projection (alias for o.hook_in)
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+import torch
+
+from transformer_lens.hook_points import HookPoint
+from transformer_lens.model_bridge.generalized_components.attention import (
+    AttentionBridge,
+)
+from transformer_lens.model_bridge.generalized_components.base import (
+    GeneralizedComponent,
+)
+from transformer_lens.model_bridge.generalized_components.position_embedding_hooks_mixin import (
+    PositionEmbeddingHooksMixin,
+)
+
+
+def _rotate_half(x: torch.Tensor) -> torch.Tensor:
+    """Rotate half of the hidden dims of the input (standard RoPE helper)."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def _apply_rotary_pos_emb(
+    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Apply rotary position embedding to q and k tensors."""
+    cos = cos.unsqueeze(1)  # [batch, 1, seq, dim]
+    sin = sin.unsqueeze(1)
+    q_embed = (q * cos) + (_rotate_half(q) * sin)
+    k_embed = (k * cos) + (_rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MLAAttentionBridge(PositionEmbeddingHooksMixin, AttentionBridge):
+    """Bridge for DeepSeek's Multi-Head Latent Attention (MLA).
+
+    Reimplements the MLA forward path with hooks at each computation stage.
+    Standard W_Q/W_K/W_V properties are not available on MLA models — use
+    the submodule weight access (q_a_proj, q_b_proj, etc.) instead.
+    """
+
+    # Override property_aliases from AttentionBridge — MLA has no q/k/v submodules
+    # in the standard sense. Accessing W_Q etc. would be misleading.
+    property_aliases: Dict[str, str] = {}
+
+    hook_aliases = {
+        "hook_result": "hook_out",
+        "hook_z": "o.hook_in",
+    }
+
+    def __init__(
+        self,
+        name: str,
+        config: Any,
+        submodules: Optional[Dict[str, GeneralizedComponent]] = None,
+        **kwargs: Any,
+    ):
+        super().__init__(name, config, submodules=submodules, **kwargs)
+        self._init_position_embedding_hooks()
+
+        # MLA-specific hooks for compressed latent representations
+        self.hook_q_latent = HookPoint()
+        self.hook_kv_latent = HookPoint()
+
+        # Final Q/K/V hooks (post-decompression, post-RoPE)
+        self.hook_q = HookPoint()
+        self.hook_k = HookPoint()
+        self.hook_v = HookPoint()
+
+        # RoPE hooks for the rope portion splits
+        self.hook_rot_q = HookPoint()
+        self.hook_rot_k = HookPoint()
+
+        # MLA params are read from the HF attention module at first forward(),
+        # not from the bridge config — TransformerBridgeConfig doesn't propagate
+        # MLA-specific fields (q_lora_rank, kv_lora_rank, etc.).
+        self._mla_params_initialized = False
+
+    def forward(self, *args: Any, **kwargs: Any) -> Any:
+        """Reimplemented MLA forward with hooks at each computation stage.
+
+        Follows the DeepseekV3Attention forward path, calling into HF submodules
+        individually and firing hooks at each meaningful stage.
+        """
+        if self.original_component is None:
+            raise RuntimeError(
+                f"Original component not set for {self.name}. "
+                "Call set_original_component() first."
+            )
+
+        hf_attn: Any = self.original_component
+
+        # Lazy-init MLA params from the HF attention module (not from bridge config,
+        # which doesn't propagate MLA-specific fields).
+        if not self._mla_params_initialized:
+            self._q_lora_rank = getattr(hf_attn, "q_lora_rank", None)
+            self._kv_lora_rank = getattr(hf_attn, "kv_lora_rank", 512)
+            self._qk_nope_head_dim = getattr(hf_attn, "qk_nope_head_dim", 128)
+            self._qk_rope_head_dim = getattr(hf_attn, "qk_rope_head_dim", 64)
+            self._v_head_dim = getattr(hf_attn, "v_head_dim", 128)
+            self._qk_head_dim = self._qk_nope_head_dim + self._qk_rope_head_dim
+            self._n_heads = getattr(hf_attn, "num_heads", 32)
+            hf_config = getattr(hf_attn, "config", None)
+            self._rope_interleave = (
+                getattr(hf_config, "rope_interleave", False) if hf_config else False
+            )
+            self._mla_params_initialized = True
+
+        # --- Extract inputs ---
+        if "hidden_states" in kwargs:
+            hidden_states = kwargs.pop("hidden_states")
+        elif len(args) > 0 and isinstance(args[0], torch.Tensor):
+            hidden_states = args[0]
+            args = args[1:]
+        else:
+            raise ValueError("Could not find hidden_states in args or kwargs")
+
+        position_embeddings = kwargs.pop("position_embeddings", None)
+        attention_mask = kwargs.pop("attention_mask", None)
+
+        hidden_states = self.hook_in(hidden_states)
+
+        batch_size, seq_length = hidden_states.shape[:2]
+
+        # --- Query path ---
+        if self._q_lora_rank is None:
+            # Direct projection (no compression)
+            q_states = hf_attn.q_proj(hidden_states)
+        else:
+            # Two-stage compression: q_a_proj → q_a_layernorm → q_b_proj
+            q_compressed = hf_attn.q_a_proj(hidden_states)
+            q_compressed = hf_attn.q_a_layernorm(q_compressed)
+            q_compressed = self.hook_q_latent(q_compressed)
+            q_states = hf_attn.q_b_proj(q_compressed)
+
+        # Reshape to [batch, n_heads, seq, qk_head_dim]
+        q_states = q_states.view(batch_size, seq_length, -1, self._qk_head_dim).transpose(1, 2)
+        # Split into nope (non-RoPE) and pe (RoPE) portions
+        q_pass, q_rot = torch.split(
+            q_states, [self._qk_nope_head_dim, self._qk_rope_head_dim], dim=-1
+        )
+
+        # --- KV path ---
+        # kv_a_proj_with_mqa outputs [compressed_kv || k_pe]
+        compressed_kv_full = hf_attn.kv_a_proj_with_mqa(hidden_states)
+        # Split: compressed KV latent (for kv_b_proj) and k rope portion (for direct RoPE)
+        # Note: k_pe is split off here and goes directly to RoPE — hook_kv_latent
+        # captures only the compressed_kv portion that enters the decompression path.
+        k_pass, k_rot = torch.split(
+            compressed_kv_full, [self._kv_lora_rank, self._qk_rope_head_dim], dim=-1
+        )
+
+        # Compress → normalize → decompress the KV latent
+        k_pass = hf_attn.kv_a_layernorm(k_pass)
+        k_pass = self.hook_kv_latent(k_pass)
+        k_pass = hf_attn.kv_b_proj(k_pass)
+
+        # Reshape to [batch, n_heads, seq, nope+v_head]
+        key_shape = (batch_size, seq_length, -1, self._qk_nope_head_dim + self._v_head_dim)
+        k_pass = k_pass.view(key_shape).transpose(1, 2)
+        # Split K nope portion and V
+        k_pass, value_states = torch.split(
+            k_pass, [self._qk_nope_head_dim, self._v_head_dim], dim=-1
+        )
+
+        # k_rot is [batch, seq, rope_dim] → [batch, 1, seq, rope_dim] for broadcasting
+        k_rot = k_rot.view(batch_size, 1, seq_length, self._qk_rope_head_dim)
+
+        # --- RoPE ---
+        if position_embeddings is not None:
+            position_embeddings = self._apply_position_embedding_hooks(position_embeddings)
+            cos, sin = position_embeddings
+        elif self._rotary_emb is not None:
+            # Fallback: compute from rotary_emb if position_embeddings not passed
+            position_ids = torch.arange(seq_length, device=hidden_states.device).unsqueeze(0)
+            cos, sin = self._rotary_emb(hidden_states, position_ids)
+        else:
+            raise ValueError(
+                "MLAAttentionBridge requires position_embeddings or set_rotary_emb() "
+                "to be called before forward."
+            )
+
+        q_rot, k_rot = _apply_rotary_pos_emb(q_rot, k_rot, cos, sin)
+        q_rot = self.hook_rot_q(q_rot)
+        k_rot = self.hook_rot_k(k_rot)
+
+        # Expand k_rot to match the number of heads
+        k_rot = k_rot.expand(*k_pass.shape[:-1], -1)
+
+        # Concatenate nope + rope portions to form final Q and K
+        query_states = torch.cat((q_pass, q_rot), dim=-1)
+        key_states = torch.cat((k_pass, k_rot), dim=-1)
+
+        # Fire final Q/K/V hooks — these are the tensors entering attention
+        query_states = self.hook_q(query_states)
+        key_states = self.hook_k(key_states)
+        value_states = self.hook_v(value_states)
+
+        # --- KV Cache ---
+        past_key_values = kwargs.pop("past_key_values", None)
+        cache_position = kwargs.pop("cache_position", None)
+        if past_key_values is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(
+                key_states, value_states, hf_attn.layer_idx, cache_kwargs
+            )
+
+        # --- Attention computation ---
+        # Note: No V padding needed — the bridge uses eager attention (not flash),
+        # and eager attention handles qk_head_dim != v_head_dim natively via matmul.
+        # HF only pads V when flash attention is requested.
+        scaling = self._qk_head_dim ** (-0.5)
+        attn_scores = torch.matmul(query_states, key_states.transpose(-2, -1)) * scaling
+
+        if attention_mask is not None:
+            attn_scores = attn_scores + attention_mask
+
+        attn_scores = self.hook_attn_scores(attn_scores)
+        # Upcast softmax to fp32 for numerical stability, matching HF eager attention
+        attn_weights = self._softmax_dropout_pattern(
+            attn_scores, upcast_to_fp32=True, target_dtype=query_states.dtype
+        )
+
+        # Weighted sum of values
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        # --- Output projection ---
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, seq_length, -1)
+        attn_output = hf_attn.o_proj(attn_output)
+
+        attn_output = self.hook_out(attn_output)
+        return attn_output, attn_weights
+
+    def __getattr__(self, name: str) -> Any:
+        """Raise clear error for standard weight properties that don't apply to MLA."""
+        if name in ("W_Q", "W_K", "W_V", "W_O", "b_Q", "b_K", "b_V", "b_O"):
+            raise NotImplementedError(
+                f"{name} is not available on MLA (Multi-Head Latent Attention) models. "
+                f"MLA uses compressed projections instead of standard Q/K/V. "
+                f"Access weights via submodules: q_a_proj, q_b_proj, kv_a_proj_with_mqa, "
+                f"kv_b_proj, o (o_proj)."
+            )
+        return super().__getattr__(name)
diff --git a/transformer_lens/model_bridge/supported_architectures/__init__.py b/transformer_lens/model_bridge/supported_architectures/__init__.py
index ac1a334e2..40673c97c 100644
--- a/transformer_lens/model_bridge/supported_architectures/__init__.py
+++ b/transformer_lens/model_bridge/supported_architectures/__init__.py
@@ -12,6 +12,9 @@
 from transformer_lens.model_bridge.supported_architectures.bloom import (
     BloomArchitectureAdapter,
 )
+from transformer_lens.model_bridge.supported_architectures.deepseek_v3 import (
+    DeepSeekV3ArchitectureAdapter,
+)
 from transformer_lens.model_bridge.supported_architectures.gemma1 import (
     Gemma1ArchitectureAdapter,
 )
@@ -128,6 +131,7 @@
     "ApertusArchitectureAdapter",
     "BertArchitectureAdapter",
     "BloomArchitectureAdapter",
+    "DeepSeekV3ArchitectureAdapter",
     "Gemma1ArchitectureAdapter",
     "Gemma2ArchitectureAdapter",
     "Gemma3ArchitectureAdapter",
diff --git a/transformer_lens/model_bridge/supported_architectures/deepseek_v3.py b/transformer_lens/model_bridge/supported_architectures/deepseek_v3.py
new file mode 100644
index 000000000..8b4166b09
--- /dev/null
+++ b/transformer_lens/model_bridge/supported_architectures/deepseek_v3.py
@@ -0,0 +1,128 @@
+"""DeepSeek V3 architecture adapter.
+
+Supports DeepSeek V3 and DeepSeek-R1 models (both use DeepseekV3ForCausalLM).
+Key features:
+- Multi-Head Latent Attention (MLA): Q and KV compressed via LoRA-style projections
+- Mixture of Experts (MoE) with shared experts on most layers
+- Dense MLP on first `first_k_dense_replace` layers
+"""
+
+from typing import Any
+
+from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter
+from transformer_lens.model_bridge.generalized_components import (
+    BlockBridge,
+    EmbeddingBridge,
+    GatedMLPBridge,
+    LinearBridge,
+    MLAAttentionBridge,
+    MoEBridge,
+    RMSNormalizationBridge,
+    RotaryEmbeddingBridge,
+    UnembeddingBridge,
+)
+from transformer_lens.model_bridge.generalized_components.base import (
+    GeneralizedComponent,
+)
+
+
+class DeepSeekV3ArchitectureAdapter(ArchitectureAdapter):
+    """Architecture adapter for DeepSeek V3 / R1 models.
+
+    DeepSeek V3 uses:
+    - RMSNorm for all normalizations
+    - Multi-Head Latent Attention (MLA) with compressed Q and KV projections
+    - Rotary position embeddings (RoPE) on partial head dimensions only
+    - Mixture of Experts MLP on most layers, dense MLP on first few layers
+    - No biases on projections
+    """
+
+    def __init__(self, cfg: Any) -> None:
+        super().__init__(cfg)
+
+        self.cfg.normalization_type = "RMS"
+        self.cfg.positional_embedding_type = "rotary"
+        self.cfg.gated_mlp = True
+        self.cfg.final_rms = True
+        self.cfg.uses_rms_norm = True
+        # Not used by MLAAttentionBridge (which reimplements forward), but needed
+        # when the HF model is used as a reference in setup_component_testing /
+        # benchmarks — SDPA doesn't support output_attentions=True.
+        self.cfg.attn_implementation = "eager"
+
+        # MLA doesn't use standard Q/K/V/O weight rearrangements
+        self.weight_processing_conversions = {}
+
+        self.component_mapping = {
+            "embed": EmbeddingBridge(name="model.embed_tokens"),
+            "rotary_emb": RotaryEmbeddingBridge(name="model.rotary_emb", config=self.cfg),
+            "blocks": BlockBridge(
+                name="model.layers",
+                submodules={
+                    "ln1": RMSNormalizationBridge(name="input_layernorm", config=self.cfg),
+                    "ln2": RMSNormalizationBridge(name="post_attention_layernorm", config=self.cfg),
+                    "attn": MLAAttentionBridge(
+                        name="self_attn",
+                        config=self.cfg,
+                        submodules={
+                            "q_a_proj": LinearBridge(name="q_a_proj"),
+                            "q_a_layernorm": RMSNormalizationBridge(
+                                name="q_a_layernorm", config=self.cfg
+                            ),
+                            "q_b_proj": LinearBridge(name="q_b_proj"),
+                            "kv_a_proj_with_mqa": LinearBridge(name="kv_a_proj_with_mqa"),
+                            "kv_a_layernorm": RMSNormalizationBridge(
+                                name="kv_a_layernorm", config=self.cfg
+                            ),
+                            "kv_b_proj": LinearBridge(name="kv_b_proj"),
+                            "o": LinearBridge(name="o_proj"),
+                        },
+                    ),
+                    # MoEBridge wraps both MoE and dense MLP layers. On dense layers
+                    # (layer_idx < first_k_dense_replace), MoE-specific submodules
+                    # (gate, shared_experts) are gracefully skipped by setup_submodules
+                    # since DeepseekV3MLP lacks those attributes. On MoE layers, all
+                    # submodules are wired and hook_router_scores fires.
+                    "mlp": MoEBridge(
+                        name="mlp",
+                        config=self.cfg,
+                        submodules={
+                            # DeepseekV3TopkRouter is a custom Module (not nn.Linear),
+                            # so we use GeneralizedComponent instead of LinearBridge.
+                            "gate": GeneralizedComponent(name="gate"),
+                            "shared_experts": GatedMLPBridge(
+                                name="shared_experts",
+                                config=self.cfg,
+                                submodules={
+                                    "gate": LinearBridge(name="gate_proj"),
+                                    "in": LinearBridge(name="up_proj"),
+                                    "out": LinearBridge(name="down_proj"),
+                                },
+                            ),
+                        },
+                    ),
+                },
+            ),
+            "ln_final": RMSNormalizationBridge(name="model.norm", config=self.cfg),
+            "unembed": UnembeddingBridge(name="lm_head"),
+        }
+
+    def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None:
+        """Set up rotary embedding references for DeepSeek V3 component testing.
+
+        Args:
+            hf_model: The HuggingFace DeepSeek V3 model instance
+            bridge_model: The TransformerBridge model (if available)
+        """
+        rotary_emb = hf_model.model.rotary_emb
+
+        # Set on live block instances (used by forward passes)
+        if bridge_model is not None and hasattr(bridge_model, "blocks"):
+            for block in bridge_model.blocks:
+                if hasattr(block, "attn"):
+                    block.attn.set_rotary_emb(rotary_emb)
+
+        # Set on template (used by get_generalized_component() callers — benchmarks,
+        # component tests)
+        attn_bridge = self.get_generalized_component("blocks.0.attn")
+        attn_bridge.set_rotary_emb(rotary_emb)

From 6e9e1bad456643140b0a4dfd21da843c4609520d Mon Sep 17 00:00:00 2001
From: jlarson4 <jonahalarson@comcast.net>
Date: Wed, 8 Apr 2026 13:35:54 -0500
Subject: [PATCH 2/2] Compatibility testing via verify models and solutions
 based on those tests

---
 .../model_bridge/test_deepseek_adapter.py     |    77 +-
 .../test_mla_attention_bridge.py              |    93 +-
 .../benchmarks/component_outputs.py           |    34 +
 .../model_bridge/component_setup.py           |     5 +-
 .../generalized_components/mla_attention.py   |    82 +-
 .../supported_architectures/deepseek_v3.py    |    37 +-
 .../tools/model_registry/__init__.py          |     1 +
 .../data/architecture_gaps.json               |  2866 ++-
 .../model_registry/data/supported_models.json | 19672 +++++++++++++++-
 .../data/verification_history.json            |   352 +-
 10 files changed, 21972 insertions(+), 1247 deletions(-)

diff --git a/tests/integration/model_bridge/test_deepseek_adapter.py b/tests/integration/model_bridge/test_deepseek_adapter.py
index 002a839f9..9409299fd 100644
--- a/tests/integration/model_bridge/test_deepseek_adapter.py
+++ b/tests/integration/model_bridge/test_deepseek_adapter.py
@@ -1,8 +1,4 @@
-"""Integration tests for DeepSeek V3 architecture adapter.
-
-Uses a tiny programmatic DeepseekV3 model (~6.5M params) saved to a temp dir,
-since no small pretrained DeepSeek V3/R1 models exist (smallest is 671B).
-"""
+"""Integration tests for DeepSeek V3 architecture adapter."""
 
 import tempfile
 
@@ -15,11 +11,10 @@
 
 @pytest.fixture(scope="module")
 def tiny_deepseek_bridge():
-    """Create a TransformerBridge wrapping a tiny programmatic DeepSeek V3 model."""
     tiny_config = DeepseekV3Config(
         hidden_size=256,
         intermediate_size=512,
-        num_hidden_layers=4,  # 1 dense + 3 MoE
+        num_hidden_layers=4,
         num_attention_heads=8,
         q_lora_rank=64,
         kv_lora_rank=32,
@@ -47,8 +42,6 @@ def tiny_deepseek_bridge():
 
 
 class TestDeepSeekBridgeCreation:
-    """Test bridge creation and structural validation."""
-
     def test_bridge_has_correct_block_count(self, tiny_deepseek_bridge):
         assert len(tiny_deepseek_bridge.blocks) == 4
 
@@ -62,13 +55,10 @@ def test_attention_is_mla(self, tiny_deepseek_bridge):
             MLAAttentionBridge,
         )
 
-        attn = tiny_deepseek_bridge.blocks[0].attn
-        assert isinstance(attn, MLAAttentionBridge)
+        assert isinstance(tiny_deepseek_bridge.blocks[0].attn, MLAAttentionBridge)
 
 
 class TestDeepSeekForwardPass:
-    """Test forward pass produces valid output."""
-
     def test_forward_returns_logits(self, tiny_deepseek_bridge):
         tokens = torch.tensor([[1, 2, 3, 4]])
         with torch.no_grad():
@@ -78,91 +68,58 @@ def test_forward_returns_logits(self, tiny_deepseek_bridge):
         assert not torch.isinf(output).any()
 
     def test_forward_matches_hf(self, tiny_deepseek_bridge):
-        """Bridge output should be close to HF model output."""
+        """SDPA vs manual matmul — small float32 differences expected."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         hf_model = tiny_deepseek_bridge.original_model
         with torch.no_grad():
             bridge_out = tiny_deepseek_bridge(tokens)
             hf_out = hf_model(tokens).logits
-        # SDPA vs eager difference expected
         max_diff = (bridge_out - hf_out).abs().max().item()
-        assert max_diff < 0.2, f"Bridge vs HF max diff = {max_diff}"
+        assert max_diff < 0.15, f"Bridge vs HF max diff = {max_diff}"
 
 
 class TestDeepSeekDenseVsMoELayers:
-    """Test that dense and MoE layers are handled correctly."""
-
     def test_dense_layer_has_no_moe_hooks(self, tiny_deepseek_bridge):
-        """Dense layer (blocks[0].mlp) should NOT have gate/shared_experts hooks."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
         cache_keys = set(cache.keys())
-
-        assert not any(
-            "blocks.0.mlp.gate" in k for k in cache_keys
-        ), "Dense layer should not have gate hooks"
-        assert not any(
-            "blocks.0.mlp.shared_experts" in k for k in cache_keys
-        ), "Dense layer should not have shared_experts hooks"
+        assert not any("blocks.0.mlp.gate" in k for k in cache_keys)
+        assert not any("blocks.0.mlp.shared_experts" in k for k in cache_keys)
 
     def test_moe_layer_has_gate_hooks(self, tiny_deepseek_bridge):
-        """MoE layer (blocks[1].mlp) SHOULD have gate hooks."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
-        cache_keys = set(cache.keys())
-
-        assert any("blocks.1.mlp.gate" in k for k in cache_keys), "MoE layer should have gate hooks"
+        assert any("blocks.1.mlp.gate" in k for k in cache.keys())
 
     def test_moe_layer_has_shared_experts_hooks(self, tiny_deepseek_bridge):
-        """MoE layer should have shared_experts hooks."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
-        cache_keys = set(cache.keys())
-
-        assert any(
-            "blocks.1.mlp.shared_experts" in k for k in cache_keys
-        ), "MoE layer should have shared_experts hooks"
+        assert any("blocks.1.mlp.shared_experts" in k for k in cache.keys())
 
     def test_both_layers_have_mlp_hooks(self, tiny_deepseek_bridge):
-        """Both dense and MoE layers should have basic hook_in/hook_out."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
-        cache_keys = set(cache.keys())
-
-        assert "blocks.0.mlp.hook_in" in cache_keys
-        assert "blocks.0.mlp.hook_out" in cache_keys
-        assert "blocks.1.mlp.hook_in" in cache_keys
-        assert "blocks.1.mlp.hook_out" in cache_keys
+        for i in [0, 1]:
+            assert f"blocks.{i}.mlp.hook_in" in cache
+            assert f"blocks.{i}.mlp.hook_out" in cache
 
     def test_both_layers_produce_non_nan(self, tiny_deepseek_bridge):
-        """Both dense and MoE layers should produce non-NaN output."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
-
         for i in [0, 1]:
-            key = f"blocks.{i}.mlp.hook_out"
-            assert key in cache, f"Missing {key}"
-            assert not torch.isnan(cache[key]).any(), f"NaN in {key}"
+            assert not torch.isnan(cache[f"blocks.{i}.mlp.hook_out"]).any()
 
 
 class TestDeepSeekAttentionHooks:
-    """Test MLA attention hooks fire on all layers."""
-
     def test_attention_hooks_fire_all_layers(self, tiny_deepseek_bridge):
-        """Attention hooks should fire on every layer."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
-        cache_keys = set(cache.keys())
-
         for i in range(4):
-            assert f"blocks.{i}.attn.hook_in" in cache_keys, f"Layer {i} missing attn.hook_in"
-            assert f"blocks.{i}.attn.hook_out" in cache_keys, f"Layer {i} missing attn.hook_out"
+            assert f"blocks.{i}.attn.hook_in" in cache
+            assert f"blocks.{i}.attn.hook_out" in cache
 
     def test_mla_latent_hooks_fire(self, tiny_deepseek_bridge):
-        """MLA-specific latent hooks should fire."""
         tokens = torch.tensor([[1, 2, 3, 4]])
         _, cache = tiny_deepseek_bridge.run_with_cache(tokens)
-        cache_keys = set(cache.keys())
-
-        assert any("hook_q_latent" in k for k in cache_keys), "hook_q_latent should fire"
-        assert any("hook_kv_latent" in k for k in cache_keys), "hook_kv_latent should fire"
+        assert any("hook_q_latent" in k for k in cache.keys())
+        assert any("hook_kv_latent" in k for k in cache.keys())
diff --git a/tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py b/tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py
index b41415797..d9824be0a 100644
--- a/tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py
+++ b/tests/unit/model_bridge/generalized_components/test_mla_attention_bridge.py
@@ -1,8 +1,4 @@
-"""Unit tests for MLAAttentionBridge (DeepSeek Multi-Head Latent Attention).
-
-Uses a tiny programmatic DeepseekV3 model (~6.5M params) since no small
-pretrained DeepSeek V3/R1 models exist.
-"""
+"""Unit tests for MLAAttentionBridge (DeepSeek Multi-Head Latent Attention)."""
 
 import pytest
 import torch
@@ -20,6 +16,7 @@ def tiny_config():
         intermediate_size=512,
         num_hidden_layers=4,
         num_attention_heads=8,
+        num_key_value_heads=1,
         q_lora_rank=64,
         kv_lora_rank=32,
         qk_nope_head_dim=16,
@@ -44,29 +41,20 @@ def tiny_model(tiny_config):
 
 @pytest.fixture(scope="module")
 def hf_attn(tiny_model):
-    """The raw HF attention module from layer 0."""
     return tiny_model.model.layers[0].self_attn
 
 
 @pytest.fixture(scope="module")
 def mla_bridge(tiny_config, hf_attn, tiny_model):
-    """An MLAAttentionBridge wrapping layer 0's attention."""
-    bridge = MLAAttentionBridge(
-        name="self_attn",
-        config=tiny_config,
-        submodules={},
-    )
+    bridge = MLAAttentionBridge(name="self_attn", config=tiny_config, submodules={})
     bridge.set_original_component(hf_attn)
     bridge.set_rotary_emb(tiny_model.model.rotary_emb)
     return bridge
 
 
 class TestMLAAttentionBridgeHooks:
-    """Test hook registration and firing."""
-
     def test_all_expected_hooks_exist(self, mla_bridge):
-        """All MLA-specific hooks should be registered."""
-        expected = [
+        for hook_name in [
             "hook_in",
             "hook_out",
             "hook_q_latent",
@@ -80,73 +68,49 @@ def test_all_expected_hooks_exist(self, mla_bridge):
             "hook_pattern",
             "hook_cos",
             "hook_sin",
-        ]
-        for hook_name in expected:
+        ]:
             assert hasattr(mla_bridge, hook_name), f"Missing hook: {hook_name}"
 
     def test_W_Q_raises_not_implemented(self, mla_bridge):
-        """Accessing W_Q on MLA should raise NotImplementedError."""
         with pytest.raises(NotImplementedError, match="not available on MLA"):
             _ = mla_bridge.W_Q
 
     def test_W_K_raises_not_implemented(self, mla_bridge):
-        """Accessing W_K on MLA should raise NotImplementedError."""
         with pytest.raises(NotImplementedError, match="not available on MLA"):
             _ = mla_bridge.W_K
 
 
 class TestMLAAttentionBridgeForward:
-    """Test forward pass correctness."""
-
     @pytest.fixture
     def sample_inputs(self, tiny_config, tiny_model):
-        """Create sample inputs for attention forward."""
         batch, seq = 2, 8
         hidden_states = torch.randn(batch, seq, tiny_config.hidden_size)
-        # Get position embeddings from the model's rotary_emb
         position_ids = torch.arange(seq).unsqueeze(0).expand(batch, -1)
         cos, sin = tiny_model.model.rotary_emb(hidden_states, position_ids)
         return hidden_states, (cos, sin)
 
     def test_output_matches_hf(self, mla_bridge, hf_attn, sample_inputs, tiny_model):
-        """Bridge forward should produce output close to HF attention.
-
-        Note: HF defaults to SDPA while the bridge reimplements with manual matmul
-        (eager-style). HF's eager attention crashes on tiny MLA configs due to
-        head count mismatches in GQA expansion. SDPA vs eager produces small
-        numerical differences (~0.01 mean, ~0.09 max in float32) which is expected.
-        """
+        """HF uses SDPA, bridge uses manual matmul — small float32 differences expected."""
         hidden_states, position_embeddings = sample_inputs
 
         with torch.no_grad():
-            # HF native forward (uses SDPA by default)
-            hf_output = hf_attn(
+            hf_attn_out = hf_attn(
                 hidden_states,
                 position_embeddings=position_embeddings,
                 attention_mask=None,
-            )
-            hf_attn_out = hf_output[0]
-
-            # Bridge forward (uses manual matmul)
-            bridge_output = mla_bridge(
+            )[0]
+            bridge_attn_out = mla_bridge(
                 hidden_states,
                 position_embeddings=position_embeddings,
                 attention_mask=None,
-            )
-            bridge_attn_out = bridge_output[0]
+            )[0]
 
-        # SDPA vs eager produces small numerical differences in float32
         max_diff = (hf_attn_out - bridge_attn_out).abs().max().item()
         mean_diff = (hf_attn_out - bridge_attn_out).abs().mean().item()
-        assert max_diff < 0.2, f"Output too different: max diff = {max_diff}"
+        assert max_diff < 0.15, f"Output too different: max diff = {max_diff}"
         assert mean_diff < 0.02, f"Output too different: mean diff = {mean_diff}"
 
     def test_hooks_fire_and_have_correct_shapes(self, mla_bridge, sample_inputs, tiny_config):
-        """All hooks should fire and produce tensors with expected shapes.
-
-        Uses PyTorch forward hooks directly since MLAAttentionBridge is a
-        GeneralizedComponent (nn.Module), not a HookedRootModule with run_with_hooks.
-        """
         hidden_states, position_embeddings = sample_inputs
         batch, seq = hidden_states.shape[:2]
         captured = {}
@@ -165,7 +129,6 @@ def test_hooks_fire_and_have_correct_shapes(self, mla_bridge, sample_inputs, tin
 
         handles = []
         for name in hooks_to_check:
-            hook_point = getattr(mla_bridge, name)
 
             def make_capture(n):
                 def hook_fn(module, input, output):
@@ -173,14 +136,12 @@ def hook_fn(module, input, output):
 
                 return hook_fn
 
-            handles.append(hook_point.register_forward_hook(make_capture(name)))
+            handles.append(getattr(mla_bridge, name).register_forward_hook(make_capture(name)))
 
         try:
             with torch.no_grad():
                 mla_bridge(
-                    hidden_states,
-                    position_embeddings=position_embeddings,
-                    attention_mask=None,
+                    hidden_states, position_embeddings=position_embeddings, attention_mask=None
                 )
         finally:
             for h in handles:
@@ -189,11 +150,9 @@ def hook_fn(module, input, output):
         n_heads = tiny_config.num_attention_heads
         qk_head_dim = tiny_config.qk_nope_head_dim + tiny_config.qk_rope_head_dim
 
-        # Verify all hooks fired
         for name in hooks_to_check:
             assert name in captured, f"Hook {name} did not fire"
 
-        # Verify shapes
         assert captured["hook_q_latent"] == (batch, seq, tiny_config.q_lora_rank)
         assert captured["hook_kv_latent"] == (batch, seq, tiny_config.kv_lora_rank)
         assert captured["hook_q"] == (batch, n_heads, seq, qk_head_dim)
@@ -203,36 +162,24 @@ def hook_fn(module, input, output):
         assert captured["hook_pattern"] == (batch, n_heads, seq, seq)
 
     def test_hook_q_is_post_rope(self, mla_bridge, sample_inputs):
-        """hook_q should capture the final Q (after RoPE concat, not pre-RoPE)."""
+        """hook_q's rope portion should match hook_rot_q."""
         hidden_states, position_embeddings = sample_inputs
         q_values: list[torch.Tensor] = []
         rot_q_values: list[torch.Tensor] = []
 
-        def capture_q(module, input, output):
-            q_values.append(output.clone())
-
-        def capture_rot_q(module, input, output):
-            rot_q_values.append(output.clone())
-
-        h1 = mla_bridge.hook_q.register_forward_hook(capture_q)
-        h2 = mla_bridge.hook_rot_q.register_forward_hook(capture_rot_q)
+        h1 = mla_bridge.hook_q.register_forward_hook(lambda m, i, o: q_values.append(o.clone()))
+        h2 = mla_bridge.hook_rot_q.register_forward_hook(
+            lambda m, i, o: rot_q_values.append(o.clone())
+        )
 
         try:
             with torch.no_grad():
                 mla_bridge(
-                    hidden_states,
-                    position_embeddings=position_embeddings,
-                    attention_mask=None,
+                    hidden_states, position_embeddings=position_embeddings, attention_mask=None
                 )
         finally:
             h1.remove()
             h2.remove()
 
-        q = q_values[0]
-        rot_q = rot_q_values[0]
-        # The rope portion of Q (last qk_rope_head_dim dims) should match hook_rot_q
         qk_rope_dim = mla_bridge._qk_rope_head_dim
-        q_rope_portion = q[..., -qk_rope_dim:]
-        assert torch.allclose(
-            q_rope_portion, rot_q, atol=1e-5
-        ), "hook_q rope portion should match hook_rot_q"
+        assert torch.allclose(q_values[0][..., -qk_rope_dim:], rot_q_values[0], atol=1e-5)
diff --git a/transformer_lens/benchmarks/component_outputs.py b/transformer_lens/benchmarks/component_outputs.py
index dcbc85cca..df0ee468d 100644
--- a/transformer_lens/benchmarks/component_outputs.py
+++ b/transformer_lens/benchmarks/component_outputs.py
@@ -439,6 +439,17 @@ def _test_component_recursive(
             if last_part in ["o", "out"]:
                 return
 
+            # Skip MLA intermediates (expect compressed-dim inputs, not hidden_states)
+            if last_part in [
+                "q_a_proj",
+                "q_a_layernorm",
+                "q_b_proj",
+                "kv_a_proj_with_mqa",
+                "kv_a_layernorm",
+                "kv_b_proj",
+            ]:
+                return
+
             # Skip virtual splits from fused projections (no standalone HF equivalent)
             if last_part in ["q", "k", "v", "gate", "in"]:
                 parent_path = ".".join(path_parts[:-1])
@@ -459,6 +470,29 @@ def _test_component_recursive(
                 except Exception:
                     pass
 
+        # Skip components not wired on this layer (per-layer or per-config variation).
+        # Only report as failure if the HF model has it but the bridge doesn't.
+        try:
+            self.adapter.get_component(self.bridge_model, component_path)
+        except (AttributeError, ValueError):
+            parts = component_path.split(".")
+            if len(parts) >= 3 and parts[1].isdigit():
+                subpath = ".".join([parts[0]] + ["{layer}"] + parts[2:])
+                # Per-layer variation: exists on some other layer (e.g., MoE vs dense)
+                for probe_layer in range(self.cfg.n_layers):
+                    probe_path = subpath.replace("{layer}", str(probe_layer))
+                    try:
+                        self.adapter.get_component(self.bridge_model, probe_path)
+                        return  # Found on another layer — skip this one
+                    except (AttributeError, ValueError):
+                        continue
+                # Per-config absence: HF model also lacks it (e.g., q_lora_rank=None)
+                try:
+                    self.adapter.get_component(self.hf_model, component_path)
+                except (AttributeError, ValueError):
+                    return
+            # Bridge is missing a component that HF has — likely misconfiguration
+
         # Test this component
         result = self._test_component(component_path, component, test_inputs)
         if result is not None:
diff --git a/transformer_lens/model_bridge/component_setup.py b/transformer_lens/model_bridge/component_setup.py
index 391724e5d..50d81cfd7 100644
--- a/transformer_lens/model_bridge/component_setup.py
+++ b/transformer_lens/model_bridge/component_setup.py
@@ -103,10 +103,7 @@ def setup_submodules(
                             original_model, remote_path
                         )
                     except AttributeError:
-                        # Graceful skip: the HF component doesn't have this submodule.
-                        # This is expected for architectures with per-layer variation
-                        # (e.g., DeepSeek's dense vs MoE layers where dense layers lack
-                        # gate/shared_experts submodules).
+                        # Expected for per-layer variation (e.g., DeepSeek dense vs MoE)
                         logger.debug(
                             "Skipping submodule '%s' — not found on %s",
                             module_name,
diff --git a/transformer_lens/model_bridge/generalized_components/mla_attention.py b/transformer_lens/model_bridge/generalized_components/mla_attention.py
index bf5822be4..c394f85a3 100644
--- a/transformer_lens/model_bridge/generalized_components/mla_attention.py
+++ b/transformer_lens/model_bridge/generalized_components/mla_attention.py
@@ -55,8 +55,7 @@ class MLAAttentionBridge(PositionEmbeddingHooksMixin, AttentionBridge):
     the submodule weight access (q_a_proj, q_b_proj, etc.) instead.
     """
 
-    # Override property_aliases from AttentionBridge — MLA has no q/k/v submodules
-    # in the standard sense. Accessing W_Q etc. would be misleading.
+    # MLA has no standard q/k/v submodules — override to empty
     property_aliases: Dict[str, str] = {}
 
     hook_aliases = {
@@ -74,22 +73,15 @@ def __init__(
         super().__init__(name, config, submodules=submodules, **kwargs)
         self._init_position_embedding_hooks()
 
-        # MLA-specific hooks for compressed latent representations
-        self.hook_q_latent = HookPoint()
-        self.hook_kv_latent = HookPoint()
+        self.hook_q_latent = HookPoint()  # Compressed Q (post q_a_layernorm)
+        self.hook_kv_latent = HookPoint()  # Compressed KV (post kv_a_layernorm)
+        self.hook_q = HookPoint()  # Final Q entering attention (post-RoPE concat)
+        self.hook_k = HookPoint()  # Final K entering attention (post-RoPE concat)
+        self.hook_v = HookPoint()  # V from kv_b_proj split
+        self.hook_rot_q = HookPoint()  # Q rope portion after RoPE
+        self.hook_rot_k = HookPoint()  # K rope portion after RoPE
 
-        # Final Q/K/V hooks (post-decompression, post-RoPE)
-        self.hook_q = HookPoint()
-        self.hook_k = HookPoint()
-        self.hook_v = HookPoint()
-
-        # RoPE hooks for the rope portion splits
-        self.hook_rot_q = HookPoint()
-        self.hook_rot_k = HookPoint()
-
-        # MLA params are read from the HF attention module at first forward(),
-        # not from the bridge config — TransformerBridgeConfig doesn't propagate
-        # MLA-specific fields (q_lora_rank, kv_lora_rank, etc.).
+        # MLA params lazy-initialized from HF module (bridge config lacks these fields)
         self._mla_params_initialized = False
 
     def forward(self, *args: Any, **kwargs: Any) -> Any:
@@ -106,8 +98,6 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
 
         hf_attn: Any = self.original_component
 
-        # Lazy-init MLA params from the HF attention module (not from bridge config,
-        # which doesn't propagate MLA-specific fields).
         if not self._mla_params_initialized:
             self._q_lora_rank = getattr(hf_attn, "q_lora_rank", None)
             self._kv_lora_rank = getattr(hf_attn, "kv_lora_rank", 512)
@@ -221,10 +211,7 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
                 key_states, value_states, hf_attn.layer_idx, cache_kwargs
             )
 
-        # --- Attention computation ---
-        # Note: No V padding needed — the bridge uses eager attention (not flash),
-        # and eager attention handles qk_head_dim != v_head_dim natively via matmul.
-        # HF only pads V when flash attention is requested.
+        # --- Attention computation (no V padding — only needed for flash attention) ---
         scaling = self._qk_head_dim ** (-0.5)
         attn_scores = torch.matmul(query_states, key_states.transpose(-2, -1)) * scaling
 
@@ -232,7 +219,6 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
             attn_scores = attn_scores + attention_mask
 
         attn_scores = self.hook_attn_scores(attn_scores)
-        # Upcast softmax to fp32 for numerical stability, matching HF eager attention
         attn_weights = self._softmax_dropout_pattern(
             attn_scores, upcast_to_fp32=True, target_dtype=query_states.dtype
         )
@@ -248,6 +234,54 @@ def forward(self, *args: Any, **kwargs: Any) -> Any:
         attn_output = self.hook_out(attn_output)
         return attn_output, attn_weights
 
+    def get_random_inputs(
+        self,
+        batch_size: int = 2,
+        seq_len: int = 8,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> Dict[str, Any]:
+        """Generate test inputs with hidden_states, position_embeddings, and attention_mask."""
+        if device is None:
+            device = torch.device("cpu")
+        if dtype is None:
+            dtype = torch.float32
+
+        # Try bridge config (d_model), then HF attention's config (hidden_size), then fallback
+        d_model = None
+        if self.config and hasattr(self.config, "d_model"):
+            d_model = self.config.d_model
+        if d_model is None and self.original_component is not None:
+            hf_cfg = getattr(self.original_component, "config", None)
+            if hf_cfg is not None:
+                d_model = getattr(hf_cfg, "hidden_size", None)
+        if d_model is None:
+            d_model = 256
+        inputs: Dict[str, Any] = {
+            "hidden_states": torch.randn(batch_size, seq_len, d_model, device=device, dtype=dtype)
+        }
+
+        # Generate position_embeddings from rotary_emb if available,
+        # otherwise create dummy (cos=1, sin=0) embeddings
+        rope_head_dim = self._qk_rope_head_dim if self._mla_params_initialized else 64
+        if self._rotary_emb is not None:
+            try:
+                dummy_input = inputs["hidden_states"]
+                position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
+                position_embeddings = self._rotary_emb(dummy_input, position_ids)
+                inputs["position_embeddings"] = position_embeddings
+            except Exception:
+                cos = torch.ones(1, seq_len, rope_head_dim, device=device, dtype=dtype)
+                sin = torch.zeros(1, seq_len, rope_head_dim, device=device, dtype=dtype)
+                inputs["position_embeddings"] = (cos, sin)
+        else:
+            cos = torch.ones(1, seq_len, rope_head_dim, device=device, dtype=dtype)
+            sin = torch.zeros(1, seq_len, rope_head_dim, device=device, dtype=dtype)
+            inputs["position_embeddings"] = (cos, sin)
+
+        inputs["attention_mask"] = None
+        return inputs
+
     def __getattr__(self, name: str) -> Any:
         """Raise clear error for standard weight properties that don't apply to MLA."""
         if name in ("W_Q", "W_K", "W_V", "W_O", "b_Q", "b_K", "b_V", "b_O"):
diff --git a/transformer_lens/model_bridge/supported_architectures/deepseek_v3.py b/transformer_lens/model_bridge/supported_architectures/deepseek_v3.py
index 8b4166b09..ef8347503 100644
--- a/transformer_lens/model_bridge/supported_architectures/deepseek_v3.py
+++ b/transformer_lens/model_bridge/supported_architectures/deepseek_v3.py
@@ -29,12 +29,8 @@
 class DeepSeekV3ArchitectureAdapter(ArchitectureAdapter):
     """Architecture adapter for DeepSeek V3 / R1 models.
 
-    DeepSeek V3 uses:
-    - RMSNorm for all normalizations
-    - Multi-Head Latent Attention (MLA) with compressed Q and KV projections
-    - Rotary position embeddings (RoPE) on partial head dimensions only
-    - Mixture of Experts MLP on most layers, dense MLP on first few layers
-    - No biases on projections
+    Uses RMSNorm, MLA with compressed Q/KV projections, partial RoPE,
+    MoE on most layers (dense MLP on first few), and no biases.
     """
 
     def __init__(self, cfg: Any) -> None:
@@ -45,12 +41,9 @@ def __init__(self, cfg: Any) -> None:
         self.cfg.gated_mlp = True
         self.cfg.final_rms = True
         self.cfg.uses_rms_norm = True
-        # Not used by MLAAttentionBridge (which reimplements forward), but needed
-        # when the HF model is used as a reference in setup_component_testing /
-        # benchmarks — SDPA doesn't support output_attentions=True.
-        self.cfg.attn_implementation = "eager"
+        # HF defaults to SDPA which handles MLA correctly.
+        # HF's eager attention crashes on MLA's asymmetric Q/K dimensions.
 
-        # MLA doesn't use standard Q/K/V/O weight rearrangements
         self.weight_processing_conversions = {}
 
         self.component_mapping = {
@@ -78,17 +71,14 @@ def __init__(self, cfg: Any) -> None:
                             "o": LinearBridge(name="o_proj"),
                         },
                     ),
-                    # MoEBridge wraps both MoE and dense MLP layers. On dense layers
-                    # (layer_idx < first_k_dense_replace), MoE-specific submodules
-                    # (gate, shared_experts) are gracefully skipped by setup_submodules
-                    # since DeepseekV3MLP lacks those attributes. On MoE layers, all
-                    # submodules are wired and hook_router_scores fires.
+                    # On dense layers (idx < first_k_dense_replace), gate and
+                    # shared_experts are gracefully skipped since DeepseekV3MLP
+                    # lacks those attributes.
                     "mlp": MoEBridge(
                         name="mlp",
                         config=self.cfg,
                         submodules={
-                            # DeepseekV3TopkRouter is a custom Module (not nn.Linear),
-                            # so we use GeneralizedComponent instead of LinearBridge.
+                            # Router is a custom Module, not nn.Linear
                             "gate": GeneralizedComponent(name="gate"),
                             "shared_experts": GatedMLPBridge(
                                 name="shared_experts",
@@ -108,21 +98,14 @@ def __init__(self, cfg: Any) -> None:
         }
 
     def setup_component_testing(self, hf_model: Any, bridge_model: Any = None) -> None:
-        """Set up rotary embedding references for DeepSeek V3 component testing.
-
-        Args:
-            hf_model: The HuggingFace DeepSeek V3 model instance
-            bridge_model: The TransformerBridge model (if available)
-        """
+        """Set up rotary embedding references for component testing."""
         rotary_emb = hf_model.model.rotary_emb
 
-        # Set on live block instances (used by forward passes)
         if bridge_model is not None and hasattr(bridge_model, "blocks"):
             for block in bridge_model.blocks:
                 if hasattr(block, "attn"):
                     block.attn.set_rotary_emb(rotary_emb)
 
-        # Set on template (used by get_generalized_component() callers — benchmarks,
-        # component tests)
+        # Also set on template for get_generalized_component() callers
         attn_bridge = self.get_generalized_component("blocks.0.attn")
         attn_bridge.set_rotary_emb(rotary_emb)
diff --git a/transformer_lens/tools/model_registry/__init__.py b/transformer_lens/tools/model_registry/__init__.py
index 7d84970f3..9c3080d7b 100644
--- a/transformer_lens/tools/model_registry/__init__.py
+++ b/transformer_lens/tools/model_registry/__init__.py
@@ -45,6 +45,7 @@
     "ApertusForCausalLM",
     "BertForMaskedLM",
     "BloomForCausalLM",
+    "DeepseekV3ForCausalLM",
     "GemmaForCausalLM",
     "Gemma2ForCausalLM",
     "Gemma3ForCausalLM",
diff --git a/transformer_lens/tools/model_registry/data/architecture_gaps.json b/transformer_lens/tools/model_registry/data/architecture_gaps.json
index 7344d18b3..9c587a2f1 100644
--- a/transformer_lens/tools/model_registry/data/architecture_gaps.json
+++ b/transformer_lens/tools/model_registry/data/architecture_gaps.json
@@ -1,129 +1,177 @@
 {
-  "generated_at": "2026-03-19",
+  "generated_at": "2026-04-08",
   "scan_info": {
-    "total_scanned": 3517,
+    "total_scanned": 5001,
     "task_filter": "text-generation",
-    "min_downloads": 500,
-    "scan_duration_seconds": 2.7
+    "min_downloads": 100,
+    "scan_duration_seconds": 2.8
   },
-  "total_unsupported_architectures": 258,
-  "total_unsupported_models": 1031,
+  "total_unsupported_architectures": 352,
+  "total_unsupported_models": 1270,
   "gaps": [
+    {
+      "architecture_id": "Qwen3_5ForConditionalGeneration",
+      "total_models": 57,
+      "sample_models": [
+        "Tesslate/OmniCoder-9B",
+        "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-NVFP4",
+        "osoleve/Qwen3.5-27B-Text-NVFP4-MTP",
+        "croll83/Qwopus3.5-27B-v3-Abliterated",
+        "nightmedia/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-qx64-hi-mlx",
+        "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2-NVFP4",
+        "Jackrong/Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled",
+        "Brooooooklyn/Qwen3.5-27B-unsloth-mlx",
+        "aifeifei798/Qwen3.5-Queen-27B",
+        "Brooooooklyn/Qwen3.5-9B-unsloth-mlx"
+      ]
+    },
+    {
+      "architecture_id": "NemotronHForCausalLM",
+      "total_models": 49,
+      "sample_models": [
+        "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
+        "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
+        "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
+        "nvidia/NVIDIA-Nemotron-Nano-9B-v2-Japanese",
+        "nvidia/NVIDIA-Nemotron-Nano-9B-v2",
+        "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
+        "nvidia/Nemotron-Cascade-2-30B-A3B",
+        "nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
+        "unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
+        "unsloth/NVIDIA-Nemotron-3-Nano-4B"
+      ]
+    },
     {
       "architecture_id": "Qwen3MoeForCausalLM",
-      "total_models": 68,
+      "total_models": 49,
       "sample_models": [
         "Qwen/Qwen3-30B-A3B",
         "Qwen/Qwen3-30B-A3B-Instruct-2507",
-        "Qwen/Qwen3-30B-A3B-Thinking-2507",
         "Qwen/Qwen3-Coder-30B-A3B-Instruct",
         "Qwen/Qwen3-235B-A22B",
+        "Qwen/Qwen3-30B-A3B-Thinking-2507",
         "trl-internal-testing/tiny-Qwen3MoeForCausalLM",
+        "nvidia/Qwen3-30B-A3B-NVFP4",
         "Qwen/Qwen3-235B-A22B-Instruct-2507",
         "Qwen/Qwen3-Coder-480B-A35B-Instruct",
-        "NVFP4/Qwen3-30B-A3B-Instruct-2507-FP4",
-        "nvidia/Qwen3-30B-A3B-NVFP4"
+        "Qwen/Qwen3-235B-A22B-Thinking-2507"
       ]
     },
     {
-      "architecture_id": "DeepseekV3ForCausalLM",
-      "total_models": 53,
+      "architecture_id": "FalconForCausalLM",
+      "total_models": 30,
       "sample_models": [
-        "deepseek-ai/DeepSeek-R1",
-        "deepseek-ai/DeepSeek-R1-0528",
-        "deepseek-ai/DeepSeek-V3",
-        "deepseek-ai/DeepSeek-V3-0324",
-        "nvidia/DeepSeek-R1-0528-NVFP4-v2",
-        "deepseek-ai/DeepSeek-V3.1",
-        "ai-sage/GigaChat3-10B-A1.8B",
-        "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
-        "nvidia/DeepSeek-V3-0324-NVFP4",
-        "moonshotai/Kimi-K2-Instruct"
+        "tiiuae/falcon-7b",
+        "tiiuae/falcon-7b-instruct",
+        "tiiuae/falcon-40b-instruct",
+        "tiiuae/falcon-40b",
+        "tiiuae/falcon-rw-1b",
+        "vilsonrodrigues/falcon-7b-instruct-sharded",
+        "explosion-testing/falcon-test",
+        "fxmarty/really-tiny-falcon-testing",
+        "tiiuae/falcon-11B",
+        "euclaise/falcon_1b_stage2"
       ]
     },
     {
-      "architecture_id": "Qwen3_5ForConditionalGeneration",
-      "total_models": 46,
+      "architecture_id": "Lfm2ForCausalLM",
+      "total_models": 29,
       "sample_models": [
-        "Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled",
-        "osoleve/Qwen3.5-27B-Text-NVFP4-MTP",
-        "Tesslate/OmniCoder-9B",
-        "nightmedia/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-qx64-hi-mlx",
-        "Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled",
-        "txn545/Qwen3.5-27B-NVFP4",
-        "mconcat/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-NVFP4",
-        "Jackrong/Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled",
-        "EganAI/qwen3.5-9b-terminal-merge",
-        "Jackrong/Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled"
+        "farbodtavakkoli/OTel-LLM-1.2B-IT",
+        "LiquidAI/LFM2.5-1.2B-Instruct",
+        "LiquidAI/LFM2-1.2B",
+        "LiquidAI/LFM2-350M",
+        "LiquidAI/LFM2.5-1.2B-Thinking",
+        "LiquidAI/LFM2.5-1.2B-Base",
+        "LiquidAI/LFM2-2.6B-Exp",
+        "LiquidAI/LFM2.5-350M",
+        "LiquidAI/LFM2-700M",
+        "unsloth/LFM2.5-1.2B-Instruct"
       ]
     },
     {
-      "architecture_id": "Qwen3NextForCausalLM",
-      "total_models": 35,
+      "architecture_id": "CodeGenForCausalLM",
+      "total_models": 29,
       "sample_models": [
-        "Qwen/Qwen3-Coder-Next",
-        "Qwen/Qwen3-Next-80B-A3B-Instruct",
-        "GadflyII/Qwen3-Coder-Next-NVFP4",
-        "nvidia/Qwen3-Next-80B-A3B-Thinking-NVFP4",
-        "nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4",
-        "Qwen/Qwen3-Next-80B-A3B-Thinking",
-        "tiny-random/qwen3-next-moe",
-        "unsloth/Qwen3-Coder-Next",
-        "yujiepan/qwen3-next-moe-tiny-random",
-        "RedHatAI/Qwen3-Coder-Next-NVFP4"
+        "Salesforce/codegen-350M-mono",
+        "Salesforce/codegen-350M-multi",
+        "Salesforce/codegen-2B-mono",
+        "Salesforce/codegen-6B-multi",
+        "Salesforce/codegen-6B-nl",
+        "Salesforce/codegen-6B-mono",
+        "Salesforce/codegen-16B-nl",
+        "Salesforce/codegen-350M-nl",
+        "Salesforce/codegen-2B-multi",
+        "dessertlab/offensive-powershell-codegen-350M-multi"
       ]
     },
     {
-      "architecture_id": "FalconForCausalLM",
-      "total_models": 32,
+      "architecture_id": "Qwen3_5ForCausalLM",
+      "total_models": 26,
       "sample_models": [
-        "tiiuae/falcon-7b",
-        "tiiuae/falcon-7b-instruct",
-        "tiiuae/falcon-40b-instruct",
-        "tiiuae/falcon-40b",
-        "tiiuae/falcon-rw-1b",
-        "fxmarty/really-tiny-falcon-testing",
-        "vilsonrodrigues/falcon-7b-instruct-sharded",
-        "tiiuae/falcon-11B",
-        "euclaise/falcon_1b_stage2",
-        "explosion-testing/falcon-test"
+        "lukey03/Qwen3.5-9B-abliterated",
+        "GoodStartLabs/gin-rummy-hbc-qwen3.5-0.8b",
+        "aifeifei798/Darkidol-Ballad-27B",
+        "brocchirodrigo/anotaai-ajuda-qwen3_5_Q4",
+        "llmfan46/Darkidol-Ballad-27B-ultra-uncensored-heretic-v1",
+        "llmfan46/Darkidol-Ballad-27B-ultra-uncensored-heretic-v2",
+        "Phonsiri/Qwen3.5-9B-Thai-Law-Base",
+        "continuum-ai/qwen3.5-4b-code-forged",
+        "aifeifei798/Darkidol-Ballad-9B",
+        "aifeifei798/Darkidol-Catgirl-9B"
+      ]
+    },
+    {
+      "architecture_id": "MPTForCausalLM",
+      "total_models": 23,
+      "sample_models": [
+        "anas-awadalla/mpt-7b",
+        "vinai/PhoGPT-4B",
+        "gl198976/mpt-7b-instruct",
+        "replit/replit-code-v1-3b",
+        "vinai/PhoGPT-4B-Chat",
+        "wtang06/mpt-125m-c4",
+        "echarlaix/tiny-mpt-random-remote-code",
+        "lightblue/japanese-mpt-7b",
+        "gl198976/mpt-7b",
+        "Nethermind/Mpt-Instruct-DotNet-S"
       ]
     },
     {
       "architecture_id": "Qwen3_5MoeForConditionalGeneration",
-      "total_models": 28,
+      "total_models": 22,
       "sample_models": [
-        "txn545/Qwen3.5-122B-A10B-NVFP4",
         "nvidia/Qwen3.5-397B-A17B-NVFP4",
-        "txn545/Qwen3.5-35B-A3B-NVFP4",
-        "RepublicOfKorokke/Qwen3.5-35B-A3B-mlx-lm-mxfp4",
-        "nightmedia/Qwen3.5-35B-A3B-Text-qx64-hi-mlx",
+        "txn545/Qwen3.5-122B-A10B-NVFP4",
+        "Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled",
         "lukealonso/Qwen3.5-397B-A17B-NVFP4",
+        "txn545/Qwen3.5-35B-A3B-NVFP4",
         "nightmedia/Qwen3.5-122B-A10B-Text-mxfp4-mlx",
         "olka-fi/Qwen3.5-122B-A10B-MXFP4",
-        "Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled",
-        "NexVeridian/Qwen3.5-35B-A3B-3bit"
+        "nightmedia/Qwen3.5-35B-A3B-Text-qx64-hi-mlx",
+        "RepublicOfKorokke/Qwen3.5-35B-A3B-mlx-lm-mxfp4",
+        "Jackrong/MLX-Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-bf16"
       ]
     },
     {
-      "architecture_id": "Lfm2ForCausalLM",
-      "total_models": 21,
+      "architecture_id": "Gemma4ForConditionalGeneration",
+      "total_models": 22,
       "sample_models": [
-        "LiquidAI/LFM2-1.2B",
-        "LiquidAI/LFM2.5-1.2B-Instruct",
-        "LiquidAI/LFM2.5-1.2B-Base",
-        "LiquidAI/LFM2-350M",
-        "LiquidAI/LFM2.5-1.2B-Thinking",
-        "LiquidAI/LFM2-2.6B",
-        "LiquidAI/LFM2-2.6B-Exp",
-        "LiquidAI/LFM2-700M",
-        "unsloth/LFM2.5-1.2B-Instruct",
-        "LiquidAI/LFM2.5-1.2B-Thinking-ONNX"
+        "nvidia/Gemma-4-31B-IT-NVFP4",
+        "dealignai/Gemma-4-31B-JANG_4M-CRACK",
+        "bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4",
+        "bg-digitalservices/Gemma-4-E2B-NVFP4A16",
+        "dealignai/Gemma-4-31B-JANG_4M-Uncensored",
+        "bg-digitalservices/Gemma-4-E2B-it-NVFP4",
+        "bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4A16",
+        "livadies/gemma-4-E2B-Ghetto-NF4",
+        "EganAI/gemma-4-31B-Claude-4.6-Opus-Reasoning-Distilled",
+        "Greytechai/Gemma-4-31B-JANG_4M-CRACK"
       ]
     },
     {
       "architecture_id": "InternLM2ForCausalLM",
-      "total_models": 19,
+      "total_models": 22,
       "sample_models": [
         "internlm/internlm2-chat-7b",
         "internlm/internlm2_5-7b-chat",
@@ -134,103 +182,87 @@
         "internlm/internlm2-base-20b",
         "chujiezheng/internlm2-chat-20b-ExPO",
         "chujiezheng/internlm2-chat-7b-ExPO",
-        "AI4Chem/ChemLLM-7B-Chat-1_5-DPO"
-      ]
-    },
-    {
-      "architecture_id": "Glm4MoeForCausalLM",
-      "total_models": 18,
-      "sample_models": [
-        "zai-org/GLM-4.5-Air",
-        "zai-org/GLM-4.7",
-        "trl-internal-testing/tiny-Glm4MoeForCausalLM",
-        "zai-org/GLM-4.5",
-        "zai-org/GLM-4.6",
-        "Tengyunw/GLM-4.7-NVFP4",
-        "Salyut1/GLM-4.7-NVFP4",
-        "np-cr/testing-glm4-moe",
-        "ArliAI/GLM-4.6-Derestricted-v3",
-        "zai-org/GLM-4.5-Air-Base"
-      ]
-    },
-    {
-      "architecture_id": "JambaForCausalLM",
-      "total_models": 17,
-      "sample_models": [
-        "ai21labs/AI21-Jamba-Mini-1.5",
-        "ai21labs/Jamba-tiny-random",
-        "ai21labs/AI21-Jamba2-3B",
-        "ai21labs/AI21-Jamba-Reasoning-3B",
-        "ai21labs/AI21-Jamba-Large-1.5",
-        "ai21labs/AI21-Jamba-Mini-1.6",
-        "ai21labs/AI21-Jamba-Large-1.6",
-        "microsoft/Dayhoff-170m-GR",
-        "ai21labs/Jamba-v0.1",
-        "microsoft/Dayhoff-170M-GRS-112000"
+        "internlm/internlm2-1_8b"
       ]
     },
     {
       "architecture_id": "QWenLMHeadModel",
-      "total_models": 16,
+      "total_models": 20,
       "sample_models": [
-        "Qwen/Qwen-7B",
+        "cckevinn/SeeClick",
         "Qwen/Qwen-7B-Chat",
         "Qwen/Qwen-VL-Chat",
+        "Qwen/Qwen-7B",
         "Qwen/Qwen-VL",
-        "Qwen/Qwen-14B-Chat-Int4",
-        "Qwen/Qwen-14B-Chat",
         "Qwen/Qwen-1_8B-Chat",
-        "Qwen/Qwen-72B",
+        "Qwen/Qwen-14B-Chat",
         "Qwen/Qwen-14B",
+        "Xingyu-Zheng/Qwen-VL-Chat",
         "Qwen/Qwen-Audio-Chat"
       ]
     },
     {
-      "architecture_id": "FalconH1ForCausalLM",
-      "total_models": 16,
+      "architecture_id": "GPTBigCodeForCausalLM",
+      "total_models": 20,
       "sample_models": [
-        "tiiuae/Falcon-H1-Tiny-90M-Instruct",
-        "tiiuae/Falcon-H1-0.5B-Base",
-        "tiiuae/Falcon-H1R-7B",
-        "tiiuae/Falcon-H1-7B-Instruct",
-        "tiiuae/Falcon-H1-34B-Base",
-        "tiiuae/Falcon-H1-34B-Instruct",
-        "tiiuae/Falcon-H1-1.5B-Base",
-        "tiiuae/Falcon-H1-7B-Base",
-        "tiiuae/Falcon-H1-3B-Base",
-        "tiiuae/Falcon-H1-1.5B-Deep-Base"
+        "bigcode/gpt_bigcode-santacoder",
+        "bigcode/tiny_starcoder_py",
+        "bigcode/starcoder",
+        "bigcode/starcoderbase-1b",
+        "ibm-granite/granite-20b-code-base-8k",
+        "GeorgiaTechResearchInstitute/starcoder-gpteacher-code-instruct",
+        "HuggingFaceH4/starchat-alpha",
+        "HuggingFaceH4/starchat-beta",
+        "defog/sqlcoder2",
+        "ibm-granite/granite-20b-code-instruct-8k"
       ]
     },
     {
-      "architecture_id": "NemotronHForCausalLM",
-      "total_models": 15,
+      "architecture_id": "Qwen3NextForCausalLM",
+      "total_models": 19,
       "sample_models": [
-        "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
-        "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
-        "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
-        "nvidia/NVIDIA-Nemotron-Nano-9B-v2-Japanese",
-        "nvidia/NVIDIA-Nemotron-Nano-9B-v2",
-        "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
-        "unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4",
-        "OpenResearcher/OpenResearcher-30B-A3B",
-        "nvidia/NVIDIA-Nemotron-Nano-9B-v2-NVFP4",
-        "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16"
+        "Qwen/Qwen3-Coder-Next",
+        "Qwen/Qwen3-Next-80B-A3B-Instruct",
+        "GadflyII/Qwen3-Coder-Next-NVFP4",
+        "unsloth/Qwen3-Coder-Next",
+        "Qwen/Qwen3-Next-80B-A3B-Thinking",
+        "tiny-random/qwen3-next-moe",
+        "yujiepan/qwen3-next-moe-tiny-random",
+        "nvidia/Qwen3-Next-80B-A3B-Instruct-NVFP4",
+        "RedHatAI/Qwen3-Coder-Next-NVFP4",
+        "nvidia/Qwen3-Next-80B-A3B-Thinking-NVFP4"
       ]
     },
     {
-      "architecture_id": "GPTBigCodeForCausalLM",
-      "total_models": 15,
+      "architecture_id": "XGLMForCausalLM",
+      "total_models": 18,
       "sample_models": [
-        "bigcode/gpt_bigcode-santacoder",
-        "bigcode/tiny_starcoder_py",
-        "bigcode/starcoder",
-        "bigcode/starcoderbase-1b",
-        "ibm-granite/granite-20b-code-base-8k",
-        "ibm-granite/granite-20b-code-instruct-8k",
-        "HuggingFaceH4/starchat-beta",
-        "HuggingFaceH4/starchat-alpha",
-        "LoupGarou/WizardCoder-Guanaco-15B-V1.1",
-        "Danielbrdz/CodeBarcenas-1b"
+        "facebook/xglm-564M",
+        "facebook/incoder-1B",
+        "facebook/xglm-7.5B",
+        "facebook/xglm-4.5B",
+        "facebook/xglm-1.7B",
+        "KoboldAI/fairseq-dense-13B",
+        "KoboldAI/fairseq-dense-2.7B",
+        "KoboldAI/fairseq-dense-125M",
+        "KoboldAI/fairseq-dense-355M",
+        "KoboldAI/fairseq-dense-1.3B"
+      ]
+    },
+    {
+      "architecture_id": "JambaForCausalLM",
+      "total_models": 16,
+      "sample_models": [
+        "ai21labs/AI21-Jamba-Mini-1.5",
+        "ai21labs/Jamba-tiny-random",
+        "ai21labs/AI21-Jamba-Mini-1.6",
+        "ai21labs/AI21-Jamba-Large-1.5",
+        "ai21labs/AI21-Jamba2-3B",
+        "ai21labs/AI21-Jamba-Large-1.6",
+        "ai21labs/Jamba-v0.1",
+        "ai21labs/AI21-Jamba-Reasoning-3B",
+        "ai21labs/AI21-Jamba2-Mini",
+        "microsoft/Dayhoff-170m-UR90"
       ]
     },
     {
@@ -241,60 +273,44 @@
         "cerebras/MiniMax-M2.1-REAP-139B-A10B",
         "MiniMaxAI/MiniMax-M2",
         "MiniMaxAI/MiniMax-M2.1",
+        "nvidia/MiniMax-M2.5-NVFP4",
         "cerebras/MiniMax-M2.5-REAP-139B-A10B",
-        "PrimeIntellect/MiniMax-M2.5-bf16",
-        "cerebras/MiniMax-M2.5-REAP-172B-A10B",
+        "amd/MiniMax-M2.5-MXFP4",
         "saricles/MiniMax-M2.5-REAP-172B-A10B-NVFP4-GB10",
         "aspctu/MiniMax-M2.5",
         "amd/MiniMax-M2.1-MXFP4"
       ]
     },
     {
-      "architecture_id": "XGLMForCausalLM",
+      "architecture_id": "FalconH1ForCausalLM",
       "total_models": 14,
       "sample_models": [
-        "facebook/xglm-564M",
-        "facebook/xglm-7.5B",
-        "facebook/xglm-1.7B",
-        "KoboldAI/fairseq-dense-13B",
-        "facebook/xglm-4.5B",
-        "KoboldAI/fairseq-dense-125M",
-        "KoboldAI/fairseq-dense-2.7B",
-        "KoboldAI/fairseq-dense-355M",
-        "KoboldAI/fairseq-dense-1.3B",
-        "KoboldAI/fairseq-dense-6.7B"
-      ]
-    },
-    {
-      "architecture_id": "Glm4MoeLiteForCausalLM",
-      "total_models": 13,
-      "sample_models": [
-        "zai-org/GLM-4.7-Flash",
-        "GadflyII/GLM-4.7-Flash-NVFP4",
-        "unsloth/GLM-4.7-Flash",
-        "GadflyII/GLM-4.7-Flash-MTP-NVFP4",
-        "Olafangensan/GLM-4.7-Flash-heretic",
-        "cerebras/GLM-4.7-Flash-REAP-23B-A3B",
-        "huihui-ai/Huihui-GLM-4.7-Flash-abliterated",
-        "TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill",
-        "Ex0bit/GLM-4.7-Flash-PRISM",
-        "MuXodious/GLM-4.7-Flash-absolute-heresy"
+        "tiiuae/Falcon-H1-0.5B-Base",
+        "tiiuae/Falcon-H1-3B-Base",
+        "tiiuae/Falcon-H1-7B-Base",
+        "tiiuae/Falcon-H1-1.5B-Deep-Base",
+        "tiiuae/Falcon-H1-34B-Base",
+        "tiiuae/Falcon-H1R-7B",
+        "tiiuae/Falcon-H1-Tiny-90M-Instruct",
+        "tiiuae/Falcon-H1-1.5B-Base",
+        "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
+        "tiiuae/Falcon-H1-7B-Instruct"
       ]
     },
     {
-      "architecture_id": "CodeGenForCausalLM",
+      "architecture_id": "DeciLMForCausalLM",
       "total_models": 13,
       "sample_models": [
-        "Salesforce/codegen-350M-mono",
-        "Salesforce/codegen-350M-multi",
-        "Salesforce/codegen-2B-mono",
-        "hf-tiny-model-private/tiny-random-CodeGenForCausalLM",
-        "Salesforce/codegen-6B-multi",
-        "shailja/fine-tuned-codegen-16B-Verilog",
-        "katuni4ka/tiny-random-codegen2",
-        "Salesforce/codegen-2B-multi",
-        "Salesforce/codegen-6B-mono",
-        "Salesforce/codegen-6B-nl"
+        "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5",
+        "nvidia/Llama-3_3-Nemotron-Super-49B-v1",
+        "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5-NVFP4",
+        "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1",
+        "nvidia/Llama-3_1-Nemotron-51B-Instruct",
+        "ConicCat/Llama3_3-Nemo-Super-Writer-49B",
+        "FriendliAI/Llama-3_3-Nemotron-Super-49B-v1_5",
+        "NewstaR/Porpoise-6b-instruct",
+        "Danielbrdz/Barcenas-6b",
+        "FriendliAI/Llama-3_1-Nemotron-Ultra-253B-v1"
       ]
     },
     {
@@ -304,29 +320,109 @@
         "RWKV/v5-Eagle-7B-HF",
         "RWKV/rwkv-4-169m-pile",
         "beomi/KoRWKV-6B",
-        "RWKV/rwkv-4-1b5-pile",
         "RWKV/rwkv-4-430m-pile",
+        "RWKV/rwkv-4-1b5-pile",
         "RWKV/rwkv-4-3b-pile",
-        "RWKV/rwkv-4-7b-pile",
         "RWKV/rwkv-raven-1b5",
-        "RWKV/rwkv-4-14b-pile",
-        "RWKV/rwkv-raven-7b"
+        "RWKV/rwkv-4-7b-pile",
+        "RWKV/rwkv-raven-3b",
+        "RWKV/rwkv-raven-14b"
       ]
     },
     {
-      "architecture_id": "DeepseekV2ForCausalLM",
-      "total_models": 11,
+      "architecture_id": "Glm4MoeForCausalLM",
+      "total_models": 12,
       "sample_models": [
-        "deepseek-ai/DeepSeek-V2-Lite-Chat",
-        "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
-        "deepseek-ai/DeepSeek-V2-Lite",
-        "deepseek-ai/DeepSeek-V2-Chat",
-        "deepseek-ai/DeepSeek-Coder-V2-Instruct-0724",
+        "zai-org/GLM-4.5-Air",
+        "zai-org/GLM-4.7",
+        "trl-internal-testing/tiny-Glm4MoeForCausalLM",
+        "zai-org/GLM-4.5",
+        "zai-org/GLM-4.6",
+        "Tengyunw/GLM-4.7-NVFP4",
+        "np-cr/testing-glm4-moe",
+        "nvidia/GLM-4.7-NVFP4",
+        "Salyut1/GLM-4.7-NVFP4",
+        "ArliAI/GLM-4.6-Derestricted-v3"
+      ]
+    },
+    {
+      "architecture_id": "BaichuanForCausalLM",
+      "total_models": 12,
+      "sample_models": [
+        "baichuan-inc/Baichuan2-7B-Chat",
+        "baichuan-inc/Baichuan2-13B-Chat",
+        "baichuan-inc/Baichuan-13B-Chat",
+        "baichuan-inc/Baichuan2-7B-Base",
+        "baichuan-inc/Baichuan2-13B-Base",
+        "katuni4ka/tiny-random-baichuan2",
+        "sakuraumi/Sakura-13B-Galgame",
+        "zxbsmk/NSFW_13B_sft",
+        "katuni4ka/tiny-random-baichuan2-13b",
+        "baichuan-inc/Baichuan-13B-Base"
+      ]
+    },
+    {
+      "architecture_id": "LLaMAForCausalLM",
+      "total_models": 12,
+      "sample_models": [
+        "maicomputer/alpaca-13b",
+        "Enoch/llama-65b-hf",
+        "mncai/chatdoctor",
+        "AdaptLLM/law-LLM",
+        "Nitish-Garikoti/finance-LLM",
+        "boboto/LLaMA-65B-HF",
+        "AdaptLLM/finance-LLM",
+        "AdaptLLM/medicine-LLM",
+        "Rardilit/Panther_v1",
+        "James-WYang/BigTranslate"
+      ]
+    },
+    {
+      "architecture_id": "DeepseekV2ForCausalLM",
+      "total_models": 11,
+      "sample_models": [
+        "deepseek-ai/DeepSeek-V2-Lite-Chat",
+        "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
+        "deepseek-ai/DeepSeek-V2-Lite",
         "deepseek-ai/DeepSeek-V2",
-        "deepseek-ai/DeepSeek-V2.5",
+        "deepseek-ai/DeepSeek-V2-Chat",
         "deepseek-ai/DeepSeek-Coder-V2-Instruct",
+        "deepseek-ai/DeepSeek-V2.5",
         "deepseek-ai/DeepSeek-V2-Chat-0628",
-        "deepseek-ai/DeepSeek-Coder-V2-Lite-Base"
+        "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
+        "deepseek-ai/DeepSeek-V2.5-1210"
+      ]
+    },
+    {
+      "architecture_id": "MT5ForConditionalGeneration",
+      "total_models": 11,
+      "sample_models": [
+        "knowledgator/IUPAC2SMILES-canonical-base",
+        "knowledgator/SMILES2IUPAC-canonical-base",
+        "bigscience/mt0-small",
+        "bigscience/mt0-base",
+        "bigscience/mt0-large",
+        "bigscience/mt0-xl",
+        "bigscience/mt0-xxl",
+        "intelia-lab-uah/mt0-base_QG_SQAC",
+        "intelia-lab-uah/mt0-base_AE_SQAC",
+        "UBC-NLP/toucan-1.2B"
+      ]
+    },
+    {
+      "architecture_id": "LlavaLlamaForCausalLM",
+      "total_models": 11,
+      "sample_models": [
+        "LanguageBind/Video-LLaVA-7B",
+        "wisdomik/Quilt-Llava-v1.5-7b",
+        "liuhaotian/llava-llama-2-13b-chat-lightning-preview",
+        "lmms-lab/llama3-llava-next-8b",
+        "mmaaz60/LLaVA-7B-Lightening-v1-1",
+        "microsoft/llava-med-7b-delta",
+        "deepcs233/VisCoT-7b-336",
+        "ManishThota/Ollama_Video_llama_7B",
+        "liuhaotian/LLaVA-Lightning-7B-delta-v1-1",
+        "EricPolaris/Quilt-Llava-v1.5-7b"
       ]
     },
     {
@@ -334,11 +430,11 @@
       "total_models": 10,
       "sample_models": [
         "trl-internal-testing/tiny-CohereForCausalLM",
-        "CohereLabs/aya-23-8B",
         "CohereLabs/aya-expanse-8b",
         "CohereLabs/c4ai-command-r-v01",
-        "CohereLabs/aya-expanse-32b",
+        "CohereLabs/aya-23-8B",
         "NLPark/AnFeng_v3_Avocet",
+        "CohereLabs/aya-expanse-32b",
         "CohereLabs/aya-23-35B",
         "CohereLabs/c4ai-command-r-plus-08-2024",
         "CohereLabs/c4ai-command-r-08-2024",
@@ -346,50 +442,49 @@
       ]
     },
     {
-      "architecture_id": "T5GemmaForConditionalGeneration",
+      "architecture_id": "MiniCPMForCausalLM",
       "total_models": 10,
+      "sample_models": [
+        "openbmb/MiniCPM-2B-sft-bf16",
+        "openbmb/MiniCPM4.1-8B",
+        "openbmb/MiniCPM-1B-sft-bf16",
+        "openbmb/MiniCPM4-0.5B",
+        "openbmb/MiniCPM-MoE-8x2B",
+        "katuni4ka/tiny-random-minicpm",
+        "openbmb/MiniCPM-S-1B-sft",
+        "openbmb/MiniCPM4-8B",
+        "openbmb/MiniCPM-2B-dpo-bf16",
+        "openbmb/MiniCPM-2B-sft-fp32"
+      ]
+    },
+    {
+      "architecture_id": "T5GemmaForConditionalGeneration",
+      "total_models": 9,
       "sample_models": [
         "google/t5gemma-s-s-prefixlm",
         "google/t5gemma-9b-9b-ul2",
         "google/t5gemma-b-b-ul2",
+        "google/t5gemma-2b-2b-prefixlm",
         "google/t5gemma-2b-2b-ul2",
+        "google/t5gemma-l-l-ul2-it",
+        "google/t5gemma-ml-ml-ul2-it",
         "google/t5gemma-b-b-prefixlm",
-        "google/t5gemma-9b-9b-ul2-it",
-        "google/t5gemma-2b-2b-prefixlm",
-        "google/t5gemma-9b-2b-ul2-it",
-        "google/t5gemma-l-l-prefixlm",
-        "harshaljanjani/tiny-t5gemma-test"
+        "google/t5gemma-s-s-prefixlm-it"
       ]
     },
     {
       "architecture_id": "Cohere2ForCausalLM",
-      "total_models": 10,
+      "total_models": 9,
       "sample_models": [
         "trl-internal-testing/tiny-Cohere2ForCausalLM",
         "CohereLabs/tiny-aya-global",
         "CohereLabs/c4ai-command-r7b-12-2024",
         "CohereLabs/tiny-aya-base",
-        "CohereLabs/c4ai-command-a-03-2025",
         "CohereLabs/c4ai-command-r7b-arabic-02-2025",
+        "CohereLabs/c4ai-command-a-03-2025",
         "CohereLabs/tiny-aya-water",
-        "CohereLabs/tiny-aya-fire",
-        "CohereLabs/command-a-reasoning-08-2025",
-        "CohereLabs/tiny-aya-earth"
-      ]
-    },
-    {
-      "architecture_id": "DeciLMForCausalLM",
-      "total_models": 9,
-      "sample_models": [
-        "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5",
-        "nvidia/Llama-3_3-Nemotron-Super-49B-v1",
-        "nvidia/Llama-3_3-Nemotron-Super-49B-v1_5-NVFP4",
-        "Deci/DeciLM-7B-instruct",
-        "Deci/DeciLM-7B",
-        "NewstaR/Porpoise-6b-instruct",
-        "Danielbrdz/Barcenas-6b",
-        "nvidia/Llama-3_1-Nemotron-Ultra-253B-v1",
-        "nvidia/Llama-3_1-Nemotron-51B-Instruct"
+        "CohereLabs/tiny-aya-earth",
+        "CohereLabs/tiny-aya-fire"
       ]
     },
     {
@@ -399,90 +494,119 @@
         "z-lab/Qwen3-4B-DFlash-b16",
         "z-lab/Qwen3-8B-DFlash-b16",
         "z-lab/Qwen3.5-9B-DFlash",
+        "z-lab/Qwen3.5-4B-DFlash",
         "z-lab/gpt-oss-20b-DFlash",
         "z-lab/gpt-oss-120b-DFlash",
-        "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat",
+        "z-lab/Qwen3.5-27B-DFlash",
         "z-lab/Qwen3.5-35B-A3B-DFlash",
-        "z-lab/Qwen3-Coder-30B-A3B-DFlash",
-        "z-lab/Qwen3.5-4B-DFlash"
+        "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat"
       ]
     },
     {
-      "architecture_id": "LlavaQwenForCausalLM",
+      "architecture_id": "HybridQwen3ForCausalLM",
+      "total_models": 9,
+      "sample_models": [
+        "amazon/GKA-primed-HQwen3-8B-Instruct",
+        "amazon/Mamba2-primed-HQwen3-8B-Instruct",
+        "amazon/GDN-primed-HQwen3-8B-Instruct",
+        "amazon/GDN-primed-HQwen3-32B-Instruct",
+        "amazon/BMOJOF-primed-HQwen3-8B-Instruct",
+        "amazon/GKA-primed-HQwen3-32B-Instruct",
+        "amazon/GKA-primed-HQwen3-8B-Reasoner",
+        "amazon/GDN-primed-HQwen3-8B-Reasoner",
+        "amazon/GKA-primed-HQwen3-32B-Reasoner"
+      ]
+    },
+    {
+      "architecture_id": "DeepseekV32ForCausalLM",
       "total_models": 8,
       "sample_models": [
-        "lmms-lab/llava-onevision-qwen2-7b-ov",
-        "lmms-lab/llava-onevision-qwen2-0.5b-ov",
-        "lmms-lab/llava-onevision-qwen2-7b-si",
-        "lmms-lab/LLaVA-Video-7B-Qwen2-Video-Only",
-        "lmms-lab/llava-onevision-qwen2-7b-ov-chat",
-        "lmms-lab/llava-next-interleave-qwen-7b",
-        "lmms-lab/llava-onevision-qwen2-0.5b-si",
-        "lmms-lab/LongVA-7B"
+        "deepseek-ai/DeepSeek-V3.2",
+        "deepseek-ai/DeepSeek-V3.2-Exp",
+        "nvidia/DeepSeek-V3.2-NVFP4",
+        "deepseek-ai/DeepSeek-V3.2-Speciale",
+        "deepseek-ai/DeepSeek-Math-V2",
+        "exolabs/DeepSeek-V3.2_bf16",
+        "deepseek-ai/DeepSeek-V3.2-Exp-Base",
+        "hyper-accel/tiny-random-deepseek-v32"
       ]
     },
     {
-      "architecture_id": "MiniCPMForCausalLM",
+      "architecture_id": "Glm4MoeLiteForCausalLM",
       "total_models": 8,
       "sample_models": [
-        "openbmb/MiniCPM4.1-8B",
-        "openbmb/MiniCPM-2B-sft-bf16",
-        "openbmb/MiniCPM4-0.5B",
-        "openbmb/MiniCPM-1B-sft-bf16",
-        "openbmb/MiniCPM-MoE-8x2B",
-        "katuni4ka/tiny-random-minicpm",
-        "openbmb/MiniCPM4-8B",
-        "openbmb/MiniCPM-S-1B-sft"
+        "zai-org/GLM-4.7-Flash",
+        "unsloth/GLM-4.7-Flash",
+        "cerebras/GLM-4.7-Flash-REAP-23B-A3B",
+        "GadflyII/GLM-4.7-Flash-NVFP4",
+        "huihui-ai/Huihui-GLM-4.7-Flash-abliterated",
+        "Olafangensan/GLM-4.7-Flash-heretic",
+        "Ex0bit/GLM-4.7-Flash-PRISM",
+        "jerrycheng233/model5_sft_16bit"
       ]
     },
     {
-      "architecture_id": "MT5ForConditionalGeneration",
+      "architecture_id": "MambaForCausalLM",
       "total_models": 8,
       "sample_models": [
-        "knowledgator/IUPAC2SMILES-canonical-base",
-        "knowledgator/SMILES2IUPAC-canonical-base",
-        "knowledgator/SMILES2IUPAC-canonical-small",
-        "bigscience/mt0-base",
-        "bigscience/mt0-small",
-        "HiTZ/Medical-mT5-large",
-        "bigscience/mt0-large",
-        "dreuxx26/Multilingual-grammar-Corrector-using-mT5-small"
+        "state-spaces/mamba-130m-hf",
+        "state-spaces/mamba-2.8b-hf",
+        "state-spaces/mamba-1.4b-hf",
+        "state-spaces/mamba-370m-hf",
+        "state-spaces/mamba-790m-hf",
+        "NYTK/PULI-HuBA-mamba-130M",
+        "EchoLabs33/mamba-130m-hxq",
+        "TRI-ML/mamba-7b-rw"
       ]
     },
     {
-      "architecture_id": "Qwen3_5ForCausalLM",
+      "architecture_id": "BloomModel",
       "total_models": 8,
       "sample_models": [
-        "lukey03/Qwen3.5-9B-abliterated",
-        "osoleve/Qwen3.5-9B-Base-Text-NVFP4",
-        "Phonsiri/Qwen3.5-9B-Thai-Law-Base",
-        "Green-eyedDevil/Monika-9B",
-        "eerwitt/qwen-h-neurons-honest",
-        "rahul7star/albeit",
-        "nbeerbower/Huihui-Qwen3.5-9B-abliterated-Grimoire-ORPO",
-        "nahidstaq/html-section-retriever"
+        "bigscience/bigscience-small-testing",
+        "TurkuNLP/gpt3-finnish-small",
+        "TurkuNLP/gpt3-finnish-large",
+        "TurkuNLP/gpt3-finnish-13B",
+        "BelleGroup/BELLE-7B-2M",
+        "norallm/norbloom-7b-scratch",
+        "TurkuNLP/gpt3-finnish-xl",
+        "Muennighoff/bloom-tiny-random"
       ]
     },
     {
-      "architecture_id": "MPTForCausalLM",
+      "architecture_id": "HunYuanDenseV1ForCausalLM",
       "total_models": 8,
       "sample_models": [
-        "anas-awadalla/mpt-7b",
-        "wtang06/mpt-125m-c4",
-        "echarlaix/tiny-mpt-random-remote-code",
-        "lightblue/japanese-mpt-7b",
-        "vinai/PhoGPT-4B",
-        "Nethermind/Mpt-Instruct-DotNet-S",
-        "replit/replit-code-v1-3b",
-        "vinai/PhoGPT-4B-Chat"
+        "tencent/Hunyuan-7B-Instruct",
+        "tencent/Hunyuan-0.5B-Pretrain",
+        "tencent/Hunyuan-1.8B-Pretrain",
+        "tencent/Hunyuan-4B-Pretrain",
+        "tencent/Hunyuan-7B-Pretrain",
+        "tencent/Hunyuan-7B-Instruct-0124",
+        "tencent/Hunyuan-1.8B-Instruct",
+        "tencent/Hunyuan-0.5B-Instruct"
+      ]
+    },
+    {
+      "architecture_id": "RWForCausalLM",
+      "total_models": 8,
+      "sample_models": [
+        "projecte-aina/aguila-7b",
+        "lightonai/alfred-40b-1023",
+        "explosion-testing/refined-web-model-test",
+        "vilm/vulture-40b",
+        "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2",
+        "nomic-ai/gpt4all-falcon",
+        "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3",
+        "OpenAssistant/falcon-40b-sft-top1-560"
       ]
     },
     {
       "architecture_id": "ExaoneForCausalLM",
       "total_models": 7,
       "sample_models": [
-        "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
         "LGAI-EXAONE/EXAONE-Deep-7.8B",
+        "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
         "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
         "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
         "LGAI-EXAONE/EXAONE-3.5-32B-Instruct",
@@ -491,149 +615,178 @@
       ]
     },
     {
-      "architecture_id": "BaichuanForCausalLM",
+      "architecture_id": "T5WithLMHeadModel",
       "total_models": 7,
       "sample_models": [
-        "baichuan-inc/Baichuan2-7B-Chat",
-        "baichuan-inc/Baichuan2-13B-Chat",
-        "baichuan-inc/Baichuan-13B-Chat",
-        "katuni4ka/tiny-random-baichuan2",
-        "baichuan-inc/Baichuan2-7B-Base",
-        "katuni4ka/tiny-random-baichuan2-13b",
-        "baichuan-inc/Baichuan2-13B-Base"
+        "unicamp-dl/ptt5-base-portuguese-vocab",
+        "Salesforce/codet5-large",
+        "Salesforce/codet5-large-ntp-py",
+        "Rostlab/prot_t5_xl_bfd",
+        "unicamp-dl/ptt5-small-portuguese-vocab",
+        "gagan3012/k2t",
+        "unicamp-dl/ptt5-large-portuguese-vocab"
       ]
     },
     {
-      "architecture_id": "SmolLM3ForCausalLM",
-      "total_models": 7,
+      "architecture_id": "BartForConditionalGeneration",
+      "total_models": 6,
       "sample_models": [
-        "HuggingFaceTB/SmolLM3-3B",
-        "HuggingFaceTB/SmolLM3-3B-Base",
-        "optimum-internal-testing/tiny-random-SmolLM3ForCausalLM",
-        "onnx-internal-testing/tiny-random-SmolLM3ForCausalLM",
-        "HuggingFaceTB/SmolLM3-3B-ONNX",
-        "N-Bot-Int/SmolSam3-MEMGRPO",
-        "toroe/SmolLM-3B-Science-ES"
+        "KomeijiForce/bart-large-emojilm",
+        "antalvdb/bart-base-spelling-nl",
+        "lmqg/bart-large-squad-qg",
+        "kengurukleo/deutsch_a2_transformer",
+        "shibing624/bart4csc-base-chinese",
+        "SkitCon/gec-spanish-BARTO-SYNTHETIC"
       ]
     },
     {
-      "architecture_id": "ProGenForCausalLM",
-      "total_models": 7,
+      "architecture_id": "GlmMoeDsaForCausalLM",
+      "total_models": 6,
       "sample_models": [
-        "hugohrban/progen2-base",
-        "hugohrban/progen2-small",
-        "hugohrban/progen2-medium",
-        "hugohrban/progen2-oas",
-        "hugohrban/progen2-small-mix7",
-        "hugohrban/progen2-large",
-        "hugohrban/progen2-xlarge"
+        "zai-org/GLM-5",
+        "nvidia/GLM-5-NVFP4",
+        "zai-org/GLM-5.1",
+        "cs2764/GLM-5-abliterated-dq4-mlx",
+        "0xSero/GLM-5-REAP-381B",
+        "cs2764/GLM-5-abliterated-dq3-mlx"
       ]
     },
     {
-      "architecture_id": "DeepseekV32ForCausalLM",
+      "architecture_id": "DreamModel",
       "total_models": 6,
       "sample_models": [
-        "deepseek-ai/DeepSeek-V3.2",
-        "deepseek-ai/DeepSeek-V3.2-Exp",
-        "nvidia/DeepSeek-V3.2-NVFP4",
-        "deepseek-ai/DeepSeek-V3.2-Speciale",
-        "deepseek-ai/DeepSeek-Math-V2",
-        "cerebras/DeepSeek-V3.2-REAP-508B-A37B"
+        "Dream-org/Dream-v0-Instruct-7B",
+        "Dream-org/Dream-v0-Base-7B",
+        "Dream-org/Dream-Coder-v0-Instruct-7B",
+        "Dream-org/Dream-Coder-v0-Base-7B",
+        "Zigeng/dParallel_Dream_7B_Instruct",
+        "Dream-org/DreamOn-v0-7B"
       ]
     },
     {
-      "architecture_id": "MambaForCausalLM",
+      "architecture_id": "Lfm2MoeForCausalLM",
       "total_models": 6,
       "sample_models": [
-        "state-spaces/mamba-130m-hf",
-        "state-spaces/mamba-2.8b-hf",
-        "state-spaces/mamba-370m-hf",
-        "state-spaces/mamba-1.4b-hf",
-        "state-spaces/mamba-790m-hf",
-        "TRI-ML/mamba-7b-rw"
+        "farbodtavakkoli/OTel-LLM-24B-IT",
+        "LiquidAI/LFM2-8B-A1B",
+        "LiquidAI/LFM2-24B-A2B",
+        "LiquidAI/LFM2-8B-A1B-ONNX",
+        "LiquidAI/LFM2-24B-A2B-ONNX",
+        "unsloth/LFM2-8B-A1B"
       ]
     },
     {
-      "architecture_id": "Qwen2MoeForCausalLM",
+      "architecture_id": "SDARForCausalLM",
       "total_models": 6,
       "sample_models": [
-        "Qwen/Qwen1.5-MoE-A2.7B",
-        "Qwen/Qwen1.5-MoE-A2.7B-Chat",
-        "Qwen/Qwen2-57B-A14B-Instruct",
-        "Qwen/Qwen2-57B-A14B",
-        "katuni4ka/tiny-random-qwen1.5-moe",
-        "yujiepan/qwen1.5-moe-tiny-random"
+        "JetLM/SDAR-1.7B-Chat",
+        "JetLM/SDAR-8B-Chat-b32",
+        "JetLM/SDAR-8B-Chat",
+        "JetLM/SDAR-4B-Chat",
+        "JetLM/SDAR-1.7B-Chat-b32",
+        "JetLM/SDAR-4B-Chat-b32"
       ]
     },
     {
-      "architecture_id": "NemotronForCausalLM",
+      "architecture_id": "AfmoeForCausalLM",
       "total_models": 6,
       "sample_models": [
-        "nvidia/Nemotron-Mini-4B-Instruct",
-        "nvidia/Minitron-8B-Base",
-        "badaoui/tiny-random-NemotronForCausalLM",
-        "nvidia/Minitron-4B-Base",
-        "thhaus/nemotron3-8b",
-        "dmvevents/Nemotron-Mini-4B-Instruct"
+        "arcee-ai/Trinity-Nano-Preview",
+        "arcee-ai/Trinity-Large-Thinking",
+        "arcee-ai/Trinity-Mini",
+        "arcee-ai/Trinity-Nano-Base",
+        "arcee-ai/Trinity-Mini-Base",
+        "arcee-ai/Trinity-Large-Preview"
       ]
     },
     {
-      "architecture_id": "HyenaDNAForCausalLM",
+      "architecture_id": "LlamaForCausalLMEagle3",
       "total_models": 6,
       "sample_models": [
-        "LongSafari/hyenadna-small-32k-seqlen-hf",
-        "LongSafari/hyenadna-tiny-1k-seqlen-hf",
-        "LongSafari/hyenadna-large-1m-seqlen-hf",
-        "LongSafari/hyenadna-medium-450k-seqlen-hf",
-        "LongSafari/hyenadna-medium-160k-seqlen-hf",
-        "LongSafari/hyenadna-tiny-1k-seqlen-d256-hf"
+        "taobao-mnn/Qwen3-VL-8B-Instruct-Eagle3",
+        "nvidia/gpt-oss-120b-Eagle3-long-context",
+        "nvidia/gpt-oss-120b-Eagle3-short-context",
+        "Zjcxy-SmartAI/Eagle3-Qwen3-32B-zh",
+        "nvidia/gpt-oss-120b-Eagle3-throughput",
+        "Zjcxy-SmartAI/Eagle3-Qwen3-8B-zh"
       ]
     },
     {
-      "architecture_id": "LlavaLlamaForCausalLM",
+      "architecture_id": "Rwkv6ForCausalLM",
       "total_models": 6,
       "sample_models": [
-        "LanguageBind/Video-LLaVA-7B",
-        "lmms-lab/llama3-llava-next-8b",
-        "liuhaotian/llava-llama-2-13b-chat-lightning-preview",
-        "wisdomik/Quilt-Llava-v1.5-7b",
-        "ManishThota/Ollama_Video_llama_7B",
-        "ShareGPTVideo/LLaVA-Hound-Pretrain"
+        "RWKV/v6-Finch-1B6-HF",
+        "RWKV/v6-Finch-7B-HF",
+        "RWKV/rwkv-6-world-1b6",
+        "RWKV/v6-Finch-14B-HF",
+        "RWKV/v6-Finch-3B-HF",
+        "RWKV/rwkv-6-world-7b"
       ]
     },
     {
-      "architecture_id": "LlavaLlamaModel",
-      "total_models": 6,
+      "architecture_id": "SmolLM3ForCausalLM",
+      "total_models": 5,
       "sample_models": [
-        "Efficient-Large-Model/VILA1.5-3b",
-        "Efficient-Large-Model/NVILA-Lite-8B",
-        "Efficient-Large-Model/NVILA-8B",
-        "Efficient-Large-Model/NVILA-15B",
-        "Efficient-Large-Model/VILA1.5-13b",
-        "Efficient-Large-Model/Llama-3-VILA1.5-8B"
+        "HuggingFaceTB/SmolLM3-3B",
+        "HuggingFaceTB/SmolLM3-3B-Base",
+        "optimum-internal-testing/tiny-random-SmolLM3ForCausalLM",
+        "unsloth/SmolLM3-3B",
+        "onnx-internal-testing/tiny-random-SmolLM3ForCausalLM"
       ]
     },
     {
-      "architecture_id": "LLaMAForCausalLM",
-      "total_models": 6,
+      "architecture_id": "XLNetLMHeadModel",
+      "total_models": 5,
       "sample_models": [
-        "Enoch/llama-65b-hf",
-        "Rardilit/Panther_v1",
-        "James-WYang/BigTranslate",
-        "mncai/chatdoctor",
-        "heegyu/LIMA-13b",
-        "maicomputer/alpaca-13b"
+        "xlnet/xlnet-base-cased",
+        "xlnet/xlnet-large-cased",
+        "hfl/chinese-xlnet-base",
+        "sshleifer/tiny-xlnet-base-cased",
+        "textattack/xlnet-base-cased-imdb"
       ]
     },
     {
-      "architecture_id": "LLaDAModelLM",
+      "architecture_id": "BioGptForCausalLM",
       "total_models": 5,
       "sample_models": [
-        "GSAI-ML/LLaDA-8B-Instruct",
-        "GSAI-ML/LLaDA-8B-Base",
-        "GSAI-ML/LLaDA-1.5",
-        "d3LLM/d3LLM_LLaDA",
-        "Fraser/LLaDA-8B-Base-gg2m"
+        "microsoft/biogpt",
+        "microsoft/BioGPT-Large",
+        "microsoft/BioGPT-Large-PubMedQA",
+        "hf-tiny-model-private/tiny-random-BioGptForCausalLM",
+        "zequnl/molxpt"
+      ]
+    },
+    {
+      "architecture_id": "Qwen2MoeForCausalLM",
+      "total_models": 5,
+      "sample_models": [
+        "Qwen/Qwen1.5-MoE-A2.7B",
+        "Qwen/Qwen1.5-MoE-A2.7B-Chat",
+        "Qwen/Qwen2-57B-A14B-Instruct",
+        "Qwen/Qwen2-57B-A14B",
+        "katuni4ka/tiny-random-qwen1.5-moe"
+      ]
+    },
+    {
+      "architecture_id": "Mistral3ForConditionalGeneration",
+      "total_models": 5,
+      "sample_models": [
+        "farbodtavakkoli/OTel-LLM-3B-IT",
+        "RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",
+        "ArmGPT/ArmenianGPT-1.0-3B",
+        "odytrice/kenichi-flash",
+        "JANGQ-AI/Mistral-Small-4-119B-A6B-JANG_2L"
+      ]
+    },
+    {
+      "architecture_id": "LLaDA2MoeModelLM",
+      "total_models": 5,
+      "sample_models": [
+        "inclusionAI/LLaDA2.1-flash",
+        "inclusionAI/LLaDA2.0-mini",
+        "inclusionAI/LLaDA2.1-mini",
+        "inclusionAI/LLaDA2.0-mini-CAP",
+        "inclusionAI/LLaDA2.0-flash"
       ]
     },
     {
@@ -641,1674 +794,2287 @@
       "total_models": 5,
       "sample_models": [
         "trl-internal-testing/tiny-FalconMambaForCausalLM",
+        "tiiuae/falcon-mamba-7b-instruct",
         "tiiuae/falcon-mamba-tiny-dev",
         "tiiuae/falcon-mamba-7b",
-        "tiiuae/falcon-mamba-7b-instruct",
         "tiiuae/Falcon3-Mamba-7B-Instruct"
       ]
     },
     {
-      "architecture_id": "DreamModel",
+      "architecture_id": "NemotronForCausalLM",
       "total_models": 5,
       "sample_models": [
-        "Dream-org/Dream-v0-Instruct-7B",
-        "Dream-org/Dream-v0-Base-7B",
-        "Dream-org/Dream-Coder-v0-Instruct-7B",
-        "d3LLM/d3LLM_Dream",
-        "Dream-org/Dream-Coder-v0-Base-7B"
+        "nvidia/Nemotron-Mini-4B-Instruct",
+        "nvidia/Minitron-8B-Base",
+        "nvidia/Minitron-4B-Base",
+        "badaoui/tiny-random-NemotronForCausalLM",
+        "thhaus/nemotron3-8b"
       ]
     },
     {
-      "architecture_id": "Eagle3Speculator",
+      "architecture_id": "HyenaDNAForCausalLM",
+      "total_models": 5,
+      "sample_models": [
+        "LongSafari/hyenadna-small-32k-seqlen-hf",
+        "LongSafari/hyenadna-medium-450k-seqlen-hf",
+        "LongSafari/hyenadna-large-1m-seqlen-hf",
+        "LongSafari/hyenadna-tiny-1k-seqlen-hf",
+        "LongSafari/hyenadna-medium-160k-seqlen-hf"
+      ]
+    },
+    {
+      "architecture_id": "ProGenForCausalLM",
+      "total_models": 5,
+      "sample_models": [
+        "hugohrban/progen2-base",
+        "hugohrban/progen2-small",
+        "hugohrban/progen2-medium",
+        "hugohrban/progen2-large",
+        "hugohrban/progen2-small-mix7"
+      ]
+    },
+    {
+      "architecture_id": "Eagle3DraftModel",
       "total_models": 5,
       "sample_models": [
-        "RedHatAI/Qwen3-8B-speculator.eagle3",
         "RedHatAI/gpt-oss-20b-speculator.eagle3",
+        "RedHatAI/gpt-oss-120b-speculator.eagle3",
+        "RedHatAI/Qwen3-30B-A3B-Instruct-2507-speculator.eagle3",
+        "RedHatAI/Qwen3-30B-A3B-Thinking-2507-speculator.eagle3",
+        "RedHatAI/Qwen3-235B-A22B-Instruct-2507-speculator.eagle3"
+      ]
+    },
+    {
+      "architecture_id": "LlavaQwen2ForCausalLM",
+      "total_models": 5,
+      "sample_models": [
+        "qnguyen3/nanoLLaVA",
+        "apple/FastVLM-0.5B",
+        "apple/FastVLM-1.5B",
+        "apple/FastVLM-7B",
+        "FreedomIntelligence/HuatuoGPT-Vision-7B"
+      ]
+    },
+    {
+      "architecture_id": "OLMoForCausalLM",
+      "total_models": 5,
+      "sample_models": [
+        "allenai/OLMo-7B-Instruct",
+        "allenai/OLMo-7B",
+        "allenai/OLMo-1B",
+        "allenai/OLMo-7B-0424",
+        "allenai/OLMo-7B-Twin-2T"
+      ]
+    },
+    {
+      "architecture_id": "MoAMetricLM",
+      "total_models": 5,
+      "sample_models": [
+        "reaperdoesntknow/MoA-150M",
+        "reaperdoesntknow/MoA-400M",
+        "reaperdoesntknow/MoA-155M",
+        "reaperdoesntknow/MoA-100M",
+        "reaperdoesntknow/DiscoverLM-70M"
+      ]
+    },
+    {
+      "architecture_id": "PldrllmForCausalLM",
+      "total_models": 5,
+      "sample_models": [
+        "fromthesky/PLDR-LLM-v51-SOC-110M-5",
+        "fromthesky/PLDR-LLM-v51-SOC-110M-2",
+        "fromthesky/PLDR-LLM-v51-SOC-110M-4",
+        "fromthesky/PLDR-LLM-v51-SOC-110M-3",
+        "fromthesky/PLDR-LLM-v51-SOC-110M-1"
+      ]
+    },
+    {
+      "architecture_id": "MBartForConditionalGeneration",
+      "total_models": 5,
+      "sample_models": [
+        "Pravopysnyk/best-unlp",
+        "DeepPavlov/mbart-large-50-ru-persona-chat",
+        "sn4kebyt3/ru-bart-large",
+        "MRNH/mbart-italian-grammar-corrector",
+        "MRNH/mbart-german-grammar-corrector"
+      ]
+    },
+    {
+      "architecture_id": "PhiMoEForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "microsoft/Phi-tiny-MoE-instruct",
+        "microsoft/Phi-mini-MoE-instruct",
+        "microsoft/Phi-3.5-MoE-instruct",
+        "optimum-intel-internal-testing/phi-3.5-moe-tiny-random"
+      ]
+    },
+    {
+      "architecture_id": "LlavaQwenForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "lmms-lab/llava-onevision-qwen2-7b-ov",
+        "lmms-lab/llava-onevision-qwen2-0.5b-ov",
+        "lmms-lab/llava-onevision-qwen2-0.5b-si",
+        "lmms-lab/llava-onevision-qwen2-7b-si"
+      ]
+    },
+    {
+      "architecture_id": "Starcoder2ForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "bigcode/starcoder2-3b",
+        "bigcode/starcoder2-7b",
+        "bigcode/starcoder2-15b",
+        "bigcode/starcoder2-15b-instruct-v0.1"
+      ]
+    },
+    {
+      "architecture_id": "Phi3VForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "microsoft/Phi-3-vision-128k-instruct",
+        "TIGER-Lab/VLM2Vec-Full",
+        "yujiepan/phi-3-vision-tiny-random",
+        "furonghuang-lab/tracevla_phi3v"
+      ]
+    },
+    {
+      "architecture_id": "Zamba2ForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "Zyphra/Zamba2-1.2B-instruct",
+        "Zyphra/Zamba2-7B-Instruct",
+        "Zyphra/Zamba2-2.7B",
+        "EchoLabs33/zamba2-1.2b-hxq"
+      ]
+    },
+    {
+      "architecture_id": "Eagle3Speculator",
+      "total_models": 4,
+      "sample_models": [
+        "RedHatAI/Qwen3-8B-speculator.eagle3",
         "RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3",
-        "RedHatAI/Qwen3-32B-speculator.eagle3",
-        "RedHatAI/Qwen3-14B-speculator.eagle3"
+        "RedHatAI/Llama-3.3-70B-Instruct-speculator.eagle3",
+        "RedHatAI/Qwen3-32B-speculator.eagle3"
+      ]
+    },
+    {
+      "architecture_id": "GlmForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "zai-org/glm-4-9b-chat-hf",
+        "zai-org/glm-4-9b-hf",
+        "zai-org/glm-edge-1.5b-chat",
+        "zai-org/glm-edge-4b-chat"
+      ]
+    },
+    {
+      "architecture_id": "OuroForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "ByteDance/Ouro-1.4B",
+        "ByteDance/Ouro-2.6B-Thinking",
+        "ByteDance/Ouro-2.6B",
+        "ByteDance/Ouro-1.4B-Thinking"
       ]
     },
     {
       "architecture_id": "SeedOssForCausalLM",
-      "total_models": 5,
+      "total_models": 4,
       "sample_models": [
         "ByteDance-Seed/Seed-OSS-36B-Instruct",
-        "ByteDance-Seed/Seed-OSS-36B-Base",
         "NousResearch/Hermes-4.3-36B",
-        "mratsim/Seed-OSS-36B-Instruct-NVFP4",
-        "YanLabs/Seed-OSS-36B-Instruct-MPOA"
+        "ByteDance-Seed/Seed-OSS-36B-Base",
+        "mratsim/Seed-OSS-36B-Instruct-NVFP4"
       ]
     },
     {
-      "architecture_id": "Ernie4_5_MoeForCausalLM",
-      "total_models": 5,
+      "architecture_id": "ArceeForCausalLM",
+      "total_models": 4,
       "sample_models": [
-        "baidu/ERNIE-4.5-21B-A3B-PT",
-        "baidu/ERNIE-4.5-21B-A3B-Base-PT",
-        "baidu/ERNIE-4.5-21B-A3B-Thinking",
-        "baidu/ERNIE-4.5-300B-A47B-PT",
-        "baidu/ERNIE-4.5-300B-A47B-Paddle"
+        "arcee-ai/AFM-4.5B-Base",
+        "optimum-intel-internal-testing/tiny-random-ArceeForCausalLM",
+        "onnx-internal-testing/tiny-random-ArceeForCausalLM",
+        "arcee-ai/AFM-4.5B"
       ]
     },
     {
-      "architecture_id": "SDARForCausalLM",
-      "total_models": 5,
+      "architecture_id": "Glm4ForCausalLM",
+      "total_models": 4,
       "sample_models": [
-        "JetLM/SDAR-8B-Chat-b32",
-        "JetLM/SDAR-4B-Chat-b32",
-        "JetLM/SDAR-8B-Chat",
-        "JetLM/SDAR-1.7B-Chat-b32",
-        "JetLM/SDAR-1.7B-Chat"
+        "zai-org/GLM-4-9B-0414",
+        "zai-org/GLM-Z1-32B-0414",
+        "zai-org/GLM-Z1-9B-0414",
+        "zai-org/GLM-4-32B-0414"
+      ]
+    },
+    {
+      "architecture_id": "BailingMoeV2ForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "inclusionAI/Ling-mini-2.0",
+        "inclusionAI/Ling-1T",
+        "inclusionAI/Ring-mini-2.0",
+        "inclusionAI/Ling-flash-2.0"
+      ]
+    },
+    {
+      "architecture_id": "AquilaForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "BAAI/AquilaChat2-7B",
+        "katuni4ka/tiny-random-aquilachat",
+        "katuni4ka/tiny-random-aquila2",
+        "BAAI/Aquila2-34B"
+      ]
+    },
+    {
+      "architecture_id": "Qwen2_5_VLForConditionalGeneration",
+      "total_models": 4,
+      "sample_models": [
+        "nvidia/Qwen2.5-VL-7B-Instruct-NVFP4",
+        "OmniSVG/OmniSVG1.1_4B",
+        "OmniSVG/OmniSVG1.1_8B",
+        "OmniSVG/OmniSVG"
+      ]
+    },
+    {
+      "architecture_id": "JAISLMHeadModel",
+      "total_models": 4,
+      "sample_models": [
+        "inceptionai/jais-13b-chat",
+        "katuni4ka/tiny-random-jais",
+        "inceptionai/jais-13b",
+        "inceptionai/jais-family-30b-8k"
+      ]
+    },
+    {
+      "architecture_id": "IdeficsForVisionText2Text",
+      "total_models": 4,
+      "sample_models": [
+        "HuggingFaceM4/idefics-80b-instruct",
+        "HuggingFaceM4/idefics-9b",
+        "HuggingFaceM4/idefics-9b-instruct",
+        "HuggingFaceM4/idefics-80b"
+      ]
+    },
+    {
+      "architecture_id": "DogeForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "SmallDoge/Doge-320M",
+        "SmallDoge/Doge-20M",
+        "SmallDoge/Doge-160M",
+        "SmallDoge/Doge-60M"
+      ]
+    },
+    {
+      "architecture_id": "MobilintLlamaForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "mobilint/Llama-3.2-3B-Instruct",
+        "mobilint/Llama-3.2-1B-Instruct",
+        "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B",
+        "mobilint/Llama-3.1-8B-Instruct"
+      ]
+    },
+    {
+      "architecture_id": "MobilintQwen2ForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "mobilint/Qwen2.5-1.5B-Instruct",
+        "mobilint/Qwen2.5-3B-Instruct",
+        "mobilint/Qwen2.5-0.5B-Instruct",
+        "mobilint/Qwen2.5-7B-Instruct"
+      ]
+    },
+    {
+      "architecture_id": "MobilintQwen3ForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "mobilint/Qwen3-4B",
+        "mobilint/Qwen3-8B",
+        "mobilint/Qwen3-1.7B",
+        "mobilint/Qwen3-0.6B"
+      ]
+    },
+    {
+      "architecture_id": "MobilintExaoneForCausalLM",
+      "total_models": 4,
+      "sample_models": [
+        "mobilint/EXAONE-3.5-2.4B-Instruct",
+        "mobilint/EXAONE-Deep-7.8B",
+        "mobilint/EXAONE-Deep-2.4B",
+        "mobilint/EXAONE-3.5-7.8B-Instruct"
+      ]
+    },
+    {
+      "architecture_id": "KimiK25ForConditionalGeneration",
+      "total_models": 3,
+      "sample_models": [
+        "nvidia/Kimi-K2.5-NVFP4",
+        "0xSero/Kimi-K2.5-PRISM-REAP-72",
+        "Ex0bit/Kimi-K2.5-PRISM-REAP-530B-A32B"
+      ]
+    },
+    {
+      "architecture_id": "LLaDAModelLM",
+      "total_models": 3,
+      "sample_models": [
+        "GSAI-ML/LLaDA-8B-Instruct",
+        "GSAI-ML/LLaDA-8B-Base",
+        "GSAI-ML/LLaDA-1.5"
+      ]
+    },
+    {
+      "architecture_id": "BambaForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "hmellor/tiny-random-BambaForCausalLM",
+        "ibm-ai-platform/Bamba-9B-v1",
+        "ibm-ai-platform/Bamba-9B-v2"
+      ]
+    },
+    {
+      "architecture_id": "InternLMForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "internlm/internlm-chat-7b",
+        "internlm/internlm-20b",
+        "internlm/internlm-7b"
+      ]
+    },
+    {
+      "architecture_id": "Exaone4ForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "LGAI-EXAONE/EXAONE-4.0-1.2B",
+        "LGAI-EXAONE/EXAONE-4.0.1-32B",
+        "LGAI-EXAONE/EXAONE-4.0-32B"
+      ]
+    },
+    {
+      "architecture_id": "OlmoHybridForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "allenai/Olmo-Hybrid-7B",
+        "allenai/Olmo-Hybrid-Instruct-DPO-7B",
+        "allenai/Olmo-Hybrid-Instruct-SFT-7B"
+      ]
+    },
+    {
+      "architecture_id": "Llama4ForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "trl-internal-testing/tiny-Llama4ForCausalLM",
+        "pruna-test/test-save-tiny-random-llama4-smashed",
+        "facebook/MobileLLM-R1.5-360M"
+      ]
+    },
+    {
+      "architecture_id": "BitNetForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "microsoft/bitnet-b1.58-2B-4T",
+        "microsoft/bitnet-b1.58-2B-4T-bf16",
+        "iSolver-AI/FEnet"
+      ]
+    },
+    {
+      "architecture_id": "IQuestCoderForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "IQuestLab/IQuest-Coder-V1-40B-Instruct",
+        "IQuestLab/IQuest-Coder-V1-7B-Instruct",
+        "Multilingual-Multimodal-NLP/IndustrialCoder"
+      ]
+    },
+    {
+      "architecture_id": "RecurrentGemmaForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "google/recurrentgemma-2b",
+        "google/recurrentgemma-2b-it",
+        "google/recurrentgemma-9b"
+      ]
+    },
+    {
+      "architecture_id": "Llama4ForConditionalGeneration",
+      "total_models": 3,
+      "sample_models": [
+        "yujiepan/llama-4-tiny-random",
+        "RedHatAI/Llama-4-Scout-17B-16E-Instruct-NVFP4",
+        "RedHatAI/Llama-4-Maverick-17B-128E-Instruct-NVFP4"
+      ]
+    },
+    {
+      "architecture_id": "LlavaLlamaModel",
+      "total_models": 3,
+      "sample_models": [
+        "Efficient-Large-Model/VILA1.5-3b",
+        "Efficient-Large-Model/NVILA-Lite-8B",
+        "Efficient-Large-Model/NVILA-8B"
+      ]
+    },
+    {
+      "architecture_id": "AraGPT2LMHeadModel",
+      "total_models": 3,
+      "sample_models": [
+        "QCRI/Fanar-2-Diwan",
+        "aubmindlab/aragpt2-mega",
+        "aubmindlab/aragpt2-large"
+      ]
+    },
+    {
+      "architecture_id": "RITAModelForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "lightonai/RITA_s",
+        "lightonai/RITA_xl",
+        "lightonai/RITA_l"
+      ]
+    },
+    {
+      "architecture_id": "NanoChatForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "Twobombs/nanochat-d34-sft-hf",
+        "pankajmathur/nanochat-d34-sft-hf",
+        "Nekochu/nanochat-d24"
+      ]
+    },
+    {
+      "architecture_id": "DuchifatCore",
+      "total_models": 3,
+      "sample_models": [
+        "Raziel1234/Duchifat-2",
+        "razielAI/Duchifat-2.1-Instruct",
+        "TopAI-1/Duchifat-2-Instruct"
+      ]
+    },
+    {
+      "architecture_id": "LISAForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "xinlai/LISA-13B-llama2-v1",
+        "xinlai/LISA-7B-v1",
+        "xinlai/LISA-7B-v1-explanatory"
+      ]
+    },
+    {
+      "architecture_id": "modeling_camelidae.LlamaForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "hywu/Camelidae-8x34B",
+        "hywu/Camelidae-8x7B",
+        "hywu/Camelidae-8x13B"
+      ]
+    },
+    {
+      "architecture_id": "ParamBharatGenForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "bharatgenai/Param-1-5B",
+        "bharatgenai/AyurParam",
+        "bharatgenai/Param-1-2.9B-Instruct"
+      ]
+    },
+    {
+      "architecture_id": "BlueLMForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "vivo-ai/BlueLM-7B-Chat",
+        "vivo-ai/BlueLM-7B-Base",
+        "vivo-ai/BlueLM-7B-Chat-32K"
+      ]
+    },
+    {
+      "architecture_id": "MptForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "explosion-testing/mpt-test",
+        "yujiepan/mpt-tiny-random",
+        "team-lucid/mptk-1b"
+      ]
+    },
+    {
+      "architecture_id": "LlamaMoEForCausalLM",
+      "total_models": 3,
+      "sample_models": [
+        "llama-moe/LLaMA-MoE-v1-3_5B-2_8",
+        "llama-moe/LLaMA-MoE-v1-3_0B-2_16",
+        "llama-moe/LLaMA-MoE-v1-3_5B-4_16"
+      ]
+    },
+    {
+      "architecture_id": "H2OVLChatModel",
+      "total_models": 2,
+      "sample_models": [
+        "h2oai/h2ovl-mississippi-800m",
+        "h2oai/h2ovl-mississippi-2b"
+      ]
+    },
+    {
+      "architecture_id": "HCXVisionV2ForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "naver-hyperclovax/HyperCLOVAX-SEED-Omni-8B",
+        "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B"
+      ]
+    },
+    {
+      "architecture_id": "OpenAIGPTLMHeadModel",
+      "total_models": 2,
+      "sample_models": [
+        "openai-community/openai-gpt",
+        "lgaalves/gpt1"
+      ]
+    },
+    {
+      "architecture_id": "SarvamMLAForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "aoxo/sarvam-105b-uncensored",
+        "sarvamai/sarvam-105b"
+      ]
+    },
+    {
+      "architecture_id": "ReformerModelWithLMHead",
+      "total_models": 2,
+      "sample_models": [
+        "google/reformer-crime-and-punishment",
+        "google/reformer-enwik8"
+      ]
+    },
+    {
+      "architecture_id": "GPTNeoXJapaneseForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "abeja/gpt-neox-japanese-2.7b",
+        "hf-tiny-model-private/tiny-random-GPTNeoXJapaneseForCausalLM"
+      ]
+    },
+    {
+      "architecture_id": "SarvamMoEForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "aoxo/sarvam-30b-uncensored",
+        "sarvamai/sarvam-30b"
+      ]
+    },
+    {
+      "architecture_id": "MiMoForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "XiaomiMiMo/MiMo-7B-Base",
+        "XiaomiMiMo/MiMo-7B-RL"
+      ]
+    },
+    {
+      "architecture_id": "StarVectorForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "starvector/starvector-1b-im2svg",
+        "starvector/starvector-8b-im2svg"
+      ]
+    },
+    {
+      "architecture_id": "Ernie4_5_MoeForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "baidu/ERNIE-4.5-21B-A3B-PT",
+        "baidu/ERNIE-4.5-21B-A3B-Base-PT"
+      ]
+    },
+    {
+      "architecture_id": "MiniMaxM1ForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "MiniMaxAI/MiniMax-M1-40k",
+        "MiniMaxAI/MiniMax-M1-80k"
+      ]
+    },
+    {
+      "architecture_id": "DeepseekForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "deepseek-ai/deepseek-moe-16b-base",
+        "deepseek-ai/deepseek-moe-16b-chat"
+      ]
+    },
+    {
+      "architecture_id": "Phi3SmallForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "microsoft/Phi-3-small-8k-instruct",
+        "microsoft/Phi-3-small-128k-instruct"
+      ]
+    },
+    {
+      "architecture_id": "OrionForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "OrionStarAI/Orion-14B-Chat",
+        "OrionStarAI/Orion-14B-Base"
+      ]
+    },
+    {
+      "architecture_id": "Ernie4_5ForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "baidu/ERNIE-4.5-0.3B-PT",
+        "baidu/ERNIE-4.5-0.3B-Base-PT"
+      ]
+    },
+    {
+      "architecture_id": "AXK1ForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "skt/A.X-K1",
+        "thkim93/axk1-2layers"
+      ]
+    },
+    {
+      "architecture_id": "NemotronFlashForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "nvidia/Nemotron-Flash-3B",
+        "nvidia/Nemotron-Flash-1B"
+      ]
+    },
+    {
+      "architecture_id": "XverseForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "xverse/XVERSE-7B-Chat",
+        "katuni4ka/tiny-random-xverse"
+      ]
+    },
+    {
+      "architecture_id": "DbrxForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "trl-internal-testing/tiny-DbrxForCausalLM",
+        "katuni4ka/tiny-random-dbrx"
+      ]
+    },
+    {
+      "architecture_id": "PersimmonForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "adept/persimmon-8b-chat",
+        "adept/persimmon-8b-base"
+      ]
+    },
+    {
+      "architecture_id": "FlexOlmoForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "allenai/Flex-reddit-2x7B-1T",
+        "allenai/FlexOlmo-7x7B-1T-RT"
+      ]
+    },
+    {
+      "architecture_id": "ChatGLMModel",
+      "total_models": 2,
+      "sample_models": [
+        "zai-org/codegeex4-all-9b",
+        "zai-org/glm-4-9b"
+      ]
+    },
+    {
+      "architecture_id": "PenguinVLQwen3ForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "tencent/Penguin-VL-8B",
+        "tencent/Penguin-VL-2B"
+      ]
+    },
+    {
+      "architecture_id": "CLIPT5ForConditionalGeneration",
+      "total_models": 2,
+      "sample_models": [
+        "zhiqiulin/clip-flant5-xl",
+        "zhiqiulin/clip-flant5-xxl"
+      ]
+    },
+    {
+      "architecture_id": "StripedHyenaModelForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "togethercomputer/evo-1-131k-base",
+        "togethercomputer/evo-1-8k-base"
+      ]
+    },
+    {
+      "architecture_id": "CrystalCoderLMHeadModel",
+      "total_models": 2,
+      "sample_models": [
+        "LLM360/Crystal",
+        "LLM360/CrystalChat"
+      ]
+    },
+    {
+      "architecture_id": "JetNemotronForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "jet-ai/Jet-Nemotron-2B",
+        "jet-ai/Jet-Nemotron-4B"
+      ]
+    },
+    {
+      "architecture_id": "MolformerForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "ibm-research/GP-MoLFormer-Uniq",
+        "ralyn/NPComposer-v2"
+      ]
+    },
+    {
+      "architecture_id": "Mamba2ForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "deqing/mamba2-300M-v5-mamba2",
+        "EchoLabs33/mamba2-1.3b-hxq"
+      ]
+    },
+    {
+      "architecture_id": "Jais2ForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "inceptionai/Jais-2-8B-Chat",
+        "inceptionai/Jais-2-70B-Chat"
+      ]
+    },
+    {
+      "architecture_id": "CogVLMForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "zai-org/cogvlm2-llama3-chat-19B",
+        "zai-org/cogvlm-chat-hf"
+      ]
+    },
+    {
+      "architecture_id": "ChatGLMForConditionalGeneration",
+      "total_models": 2,
+      "sample_models": [
+        "IAAR-Shanghai/xVerify-9B-C",
+        "qiuhuachuan/MeChat"
+      ]
+    },
+    {
+      "architecture_id": "Qwen2ForSequenceClassification",
+      "total_models": 2,
+      "sample_models": [
+        "nvidia/AceMath-7B-RM",
+        "nvidia/Qwen2.5-CascadeRL-RM-72B"
+      ]
+    },
+    {
+      "architecture_id": "RavenForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "tomg-group-umd/huginn-0125",
+        "smcleish/Recurrent-Llama-3.2-train-recurrence-32"
+      ]
+    },
+    {
+      "architecture_id": "Qwen3VLMoeForConditionalGeneration",
+      "total_models": 2,
+      "sample_models": [
+        "RedHatAI/Qwen3-VL-235B-A22B-Instruct-NVFP4",
+        "Oysiyl/qwen3-vl-30b-a3b-unslop-good-lora-v1"
+      ]
+    },
+    {
+      "architecture_id": "YoutuForCausalLM",
+      "total_models": 2,
+      "sample_models": [
+        "tencent/Youtu-LLM-2B-Base",
+        "tencent/Youtu-LLM-2B"
       ]
     },
     {
-      "architecture_id": "BloomModel",
-      "total_models": 5,
+      "architecture_id": "MosaicGPT",
+      "total_models": 2,
       "sample_models": [
-        "bigscience/bigscience-small-testing",
-        "TurkuNLP/gpt3-finnish-small",
-        "TurkuNLP/gpt3-finnish-large",
-        "TurkuNLP/gpt3-finnish-13B",
-        "TurkuNLP/gpt3-finnish-xl"
+        "anas-awadalla/mpt-1b-redpajama-200b",
+        "anas-awadalla/mpt-1b-redpajama-200b-dolly"
       ]
     },
     {
-      "architecture_id": "AfmoeForCausalLM",
-      "total_models": 5,
+      "architecture_id": "GLAForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "arcee-ai/Trinity-Nano-Preview",
-        "arcee-ai/Trinity-Mini",
-        "arcee-ai/Trinity-Large-Preview",
-        "arcee-ai/Trinity-Nano-Base",
-        "arcee-ai/Trinity-Mini-Base"
+        "fla-hub/gla-340M-15B",
+        "fla-hub/gla-1.3B-100B"
       ]
     },
     {
-      "architecture_id": "LlavaQwen2ForCausalLM",
-      "total_models": 5,
+      "architecture_id": "GPT2Model",
+      "total_models": 2,
       "sample_models": [
-        "apple/FastVLM-0.5B",
-        "qnguyen3/nanoLLaVA",
-        "apple/FastVLM-1.5B",
-        "apple/FastVLM-7B",
-        "FreedomIntelligence/HuatuoGPT-Vision-7B"
+        "cerebras/Cerebras-GPT-13B",
+        "keshan/sinhala-gpt2"
       ]
     },
     {
-      "architecture_id": "HunYuanDenseV1ForCausalLM",
-      "total_models": 5,
+      "architecture_id": "ModernBertDecoderForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "tencent/Hunyuan-7B-Instruct",
-        "tencent/Hunyuan-0.5B-Pretrain",
-        "tencent/Hunyuan-4B-Instruct",
-        "tencent/Hunyuan-0.5B-Instruct",
-        "tencent/Hunyuan-1.8B-Instruct"
+        "jhu-clsp/ettin-decoder-400m",
+        "jhu-clsp/ettin-decoder-32m"
       ]
     },
     {
-      "architecture_id": "PhiMoEForCausalLM",
-      "total_models": 4,
+      "architecture_id": "LiquidForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "microsoft/Phi-tiny-MoE-instruct",
-        "microsoft/Phi-mini-MoE-instruct",
-        "microsoft/Phi-3.5-MoE-instruct",
-        "optimum-intel-internal-testing/phi-3.5-moe-tiny-random"
+        "reaperdoesntknow/DNA-175M",
+        "reaperdoesntknow/DNA-50M"
       ]
     },
     {
-      "architecture_id": "Starcoder2ForCausalLM",
-      "total_models": 4,
+      "architecture_id": "KonkanGPT",
+      "total_models": 2,
       "sample_models": [
-        "bigcode/starcoder2-3b",
-        "bigcode/starcoder2-15b",
-        "bigcode/starcoder2-7b",
-        "bigcode/starcoder2-15b-instruct-v0.1"
+        "omdeep22/Gonyai-teo2",
+        "omdeep22/Gonyai-v1"
       ]
     },
     {
-      "architecture_id": "Lfm2MoeForCausalLM",
-      "total_models": 4,
+      "architecture_id": "BertLMHeadModel",
+      "total_models": 2,
       "sample_models": [
-        "LiquidAI/LFM2-8B-A1B",
-        "LiquidAI/LFM2-24B-A2B",
-        "huihui-ai/Huihui-LFM2-24B-A2B-abliterated",
-        "huihui-ai/Huihui-LFM2-8B-A1B-abliterated"
+        "dicta-il/BEREL_3.0",
+        "hf-tiny-model-private/tiny-random-BertLMHeadModel"
       ]
     },
     {
-      "architecture_id": "LLaDA2MoeModelLM",
-      "total_models": 4,
+      "architecture_id": "WeDLMForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "inclusionAI/LLaDA2.0-mini",
-        "inclusionAI/LLaDA2.1-mini",
-        "inclusionAI/LLaDA2.1-flash",
-        "inclusionAI/LLaDA2.0-flash"
+        "tencent/WeDLM-8B-Base",
+        "tencent/WeDLM-8B-Instruct"
       ]
     },
     {
-      "architecture_id": "LlamaForCausalLMEagle3",
-      "total_models": 4,
+      "architecture_id": "MossForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "nvidia/gpt-oss-120b-Eagle3-short-context",
-        "taobao-mnn/Qwen3-VL-8B-Instruct-Eagle3",
-        "nvidia/gpt-oss-120b-Eagle3-long-context",
-        "nvidia/gpt-oss-120b-Eagle3-throughput"
+        "OpenMOSS-Team/moss-moon-003-sft",
+        "OpenMOSS-Team/moss-moon-003-base"
       ]
     },
     {
-      "architecture_id": "DeepseekForCausalLM",
-      "total_models": 4,
+      "architecture_id": "RobertaForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "deepseek-ai/deepseek-moe-16b-base",
-        "deepseek-ai/deepseek-moe-16b-chat",
-        "ai-sage/GigaChat-20B-A3B-base",
-        "ai-sage/GigaChat-20B-A3B-instruct"
+        "gokceuludogan/ChemBERTaLM",
+        "uf-aice-lab/math-roberta"
       ]
     },
     {
-      "architecture_id": "OlmoHybridForCausalLM",
-      "total_models": 4,
+      "architecture_id": "BitnetForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "allenai/Olmo-Hybrid-7B",
-        "allenai/Olmo-Hybrid-Instruct-DPO-7B",
-        "allenai/Olmo-Hybrid-Instruct-SFT-7B",
-        "allenai/Olmo-Hybrid-Think-SFT-7B"
+        "1bitLLM/bitnet_b1_58-large",
+        "1bitLLM/bitnet_b1_58-3B"
       ]
     },
     {
-      "architecture_id": "OuroForCausalLM",
-      "total_models": 4,
+      "architecture_id": "Rwkv5ForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "ByteDance/Ouro-1.4B",
-        "ByteDance/Ouro-2.6B-Thinking",
-        "ByteDance/Ouro-1.4B-Thinking",
-        "ByteDance/Ouro-2.6B"
+        "RWKV/rwkv-5-world-3b",
+        "RWKV/rwkv-5-world-1b5"
       ]
     },
     {
-      "architecture_id": "Glm4ForCausalLM",
-      "total_models": 4,
+      "architecture_id": "BartForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "zai-org/GLM-4-9B-0414",
-        "zai-org/GLM-Z1-32B-0414",
-        "zai-org/GLM-Z1-9B-0414",
-        "zai-org/GLM-4-32B-0414"
+        "sanchit-gandhi/tiny-random-bart-fp16",
+        "hf-tiny-model-private/tiny-random-BartForCausalLM"
       ]
     },
     {
-      "architecture_id": "ArceeForCausalLM",
-      "total_models": 4,
+      "architecture_id": "Int8OPTForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "optimum-intel-internal-testing/tiny-random-ArceeForCausalLM",
-        "arcee-ai/AFM-4.5B-Base",
-        "onnx-internal-testing/tiny-random-ArceeForCausalLM",
-        "arcee-ai/AFM-4.5B"
+        "mit-han-lab/opt-125m-smoothquant",
+        "mit-han-lab/opt-6.7b-smoothquant"
       ]
     },
     {
-      "architecture_id": "BailingMoeV2ForCausalLM",
-      "total_models": 4,
+      "architecture_id": "Olmo2ForSequenceClassification",
+      "total_models": 2,
       "sample_models": [
-        "inclusionAI/Ling-mini-2.0",
-        "inclusionAI/Ling-1T",
-        "inclusionAI/Ring-mini-2.0",
-        "inclusionAI/Ling-flash-2.0"
+        "allenai/OLMo-2-1124-7B-RM",
+        "LifeWiki-ai/OLMo-2-1124-7B-RM"
       ]
     },
     {
-      "architecture_id": "AquilaForCausalLM",
-      "total_models": 4,
+      "architecture_id": "TranceptionLMHeadModel",
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       "sample_models": [
-        "BAAI/AquilaChat2-7B",
-        "katuni4ka/tiny-random-aquila2",
-        "BAAI/Aquila2-34B",
-        "katuni4ka/tiny-random-aquilachat"
+        "PascalNotin/Tranception_Large",
+        "PascalNotin/Tranception_Small"
       ]
     },
     {
-      "architecture_id": "RWForCausalLM",
-      "total_models": 4,
+      "architecture_id": "GPT",
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       "sample_models": [
-        "lightonai/alfred-40b-1023",
-        "vilm/vulture-40b",
-        "explosion-testing/refined-web-model-test",
-        "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2"
+        "LH-Tech-AI/Apex-1.5-Coder-Instruct-350M",
+        "LH-Tech-AI/Apex-1.5-Instruct-350M"
       ]
     },
     {
-      "architecture_id": "XLNetLMHeadModel",
-      "total_models": 3,
+      "architecture_id": "MultiScaleForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "xlnet/xlnet-base-cased",
-        "xlnet/xlnet-large-cased",
-        "sshleifer/tiny-xlnet-base-cased"
+        "KoinicLabs/AXL-Vision-v2",
+        "KoinicLabs/AXL-Translate"
       ]
     },
     {
-      "architecture_id": "BioGptForCausalLM",
-      "total_models": 3,
+      "architecture_id": "BolmoForCausalLM",
+      "total_models": 2,
       "sample_models": [
-        "microsoft/biogpt",
-        "microsoft/BioGPT-Large",
-        "microsoft/BioGPT-Large-PubMedQA"
+        "allenai/Bolmo-1B",
+        "allenai/Bolmo-7B"
       ]
     },
     {
-      "architecture_id": "BambaForCausalLM",
-      "total_models": 3,
+      "architecture_id": "SolarOpenForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "hmellor/tiny-random-BambaForCausalLM",
-        "ibm-ai-platform/Bamba-9B-v1",
-        "ibm-ai-platform/Bamba-9B-v2"
+        "upstage/Solar-Open-100B"
       ]
     },
     {
-      "architecture_id": "Exaone4ForCausalLM",
-      "total_models": 3,
+      "architecture_id": "MoshiForConditionalGeneration",
+      "total_models": 1,
       "sample_models": [
-        "LGAI-EXAONE/EXAONE-4.0.1-32B",
-        "LGAI-EXAONE/EXAONE-4.0-1.2B",
-        "LGAI-EXAONE/EXAONE-4.0-32B"
+        "kmhf/hf-moshiko"
       ]
     },
     {
-      "architecture_id": "MiMoForCausalLM",
-      "total_models": 3,
+      "architecture_id": "T5EncoderModel",
+      "total_models": 1,
       "sample_models": [
-        "XiaomiMiMo/MiMo-7B-Base",
-        "XiaomiMiMo/MiMo-7B-RL",
-        "XiaomiMiMo/MiMo-7B-SFT"
+        "XLabs-AI/xflux_text_encoders"
       ]
     },
     {
-      "architecture_id": "T5WithLMHeadModel",
-      "total_models": 3,
+      "architecture_id": "Step3p5ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "Rostlab/prot_t5_xl_bfd",
-        "Salesforce/codet5-large",
-        "unicamp-dl/ptt5-base-portuguese-vocab"
+        "stepfun-ai/Step-3.5-Flash"
       ]
     },
     {
-      "architecture_id": "GlmMoeDsaForCausalLM",
-      "total_models": 3,
+      "architecture_id": "AprielForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "zai-org/GLM-5",
-        "nvidia/GLM-5-NVFP4",
-        "cs2764/GLM-5_dq3-mlx"
+        "ServiceNow-AI/Apriel-5B-Instruct"
       ]
     },
     {
-      "architecture_id": "Step3p5ForCausalLM",
-      "total_models": 3,
+      "architecture_id": "IlamaForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "stepfun-ai/Step-3.5-Flash",
-        "tacos4me/Step-3.5-Flash-NVFP4",
-        "stepfun-ai/Step-3.5-Flash-Base"
+        "hmellor/Ilama-3.2-1B"
       ]
     },
     {
-      "architecture_id": "Zamba2ForCausalLM",
-      "total_models": 3,
+      "architecture_id": "XCurOSForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "Zyphra/Zamba2-1.2B-instruct",
-        "Zyphra/Zamba2-7B-Instruct",
-        "Zyphra/Zamba2-2.7B"
+        "XCurOS/XCurOS-0.1-8B-Instruct"
       ]
     },
     {
-      "architecture_id": "InternLMForCausalLM",
-      "total_models": 3,
+      "architecture_id": "TarsierForConditionalGeneration",
+      "total_models": 1,
       "sample_models": [
-        "internlm/internlm-chat-7b",
-        "internlm/internlm-20b",
-        "internlm/internlm-7b"
+        "omni-research/Tarsier-7b"
       ]
     },
     {
-      "architecture_id": "GlmForCausalLM",
-      "total_models": 3,
+      "architecture_id": "Plamo2ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "zai-org/glm-4-9b-chat-hf",
-        "zai-org/glm-4-9b-hf",
-        "zai-org/glm-edge-1.5b-chat"
+        "pfnet/plamo-2-1b"
       ]
     },
     {
-      "architecture_id": "NemotronFlashForCausalLM",
-      "total_models": 3,
+      "architecture_id": "HCXVisionForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "nvidia/Nemotron-Flash-3B",
-        "nvidia/Nemotron-Flash-3B-Instruct",
-        "nvidia/Nemotron-Flash-1B"
+        "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B"
       ]
     },
     {
-      "architecture_id": "Mistral3ForConditionalGeneration",
-      "total_models": 3,
+      "architecture_id": "KimiLinearForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",
-        "ArmGPT/ArmenianGPT-1.0-3B",
-        "srs6901/SOLARized-GraniStral-14B_2102_YeAM-HCT_32QKV"
+        "moonshotai/Kimi-Linear-48B-A3B-Instruct"
       ]
     },
     {
-      "architecture_id": "Llama4ForConditionalGeneration",
-      "total_models": 3,
+      "architecture_id": "MiMoV2FlashForCausalLM",
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       "sample_models": [
-        "RedHatAI/Llama-4-Scout-17B-16E-Instruct-NVFP4",
-        "yujiepan/llama-4-tiny-random",
-        "RedHatAI/Llama-4-Maverick-17B-128E-Instruct-NVFP4"
+        "XiaomiMiMo/MiMo-V2-Flash"
       ]
     },
     {
-      "architecture_id": "PersimmonForCausalLM",
-      "total_models": 3,
+      "architecture_id": "BaiChuanForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "adept/persimmon-8b-chat",
-        "adept/persimmon-8b-base",
-        "pszemraj/perSLIMmon-8b-base"
+        "baichuan-inc/Baichuan-7B"
       ]
     },
     {
-      "architecture_id": "JAISLMHeadModel",
-      "total_models": 3,
+      "architecture_id": "LongcatFlashForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "inceptionai/jais-13b-chat",
-        "katuni4ka/tiny-random-jais",
-        "inceptionai/jais-13b"
+        "meituan-longcat/LongCat-Flash-Chat"
       ]
     },
     {
-      "architecture_id": "TrillionForCausalLM",
-      "total_models": 3,
+      "architecture_id": "HyperCLOVAXForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "trillionlabs/Tri-21B-Think",
-        "trillionlabs/Tri-21B",
-        "trillionlabs/Tri-21B-Think-Preview"
+        "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B"
       ]
     },
     {
-      "architecture_id": "IdeficsForVisionText2Text",
-      "total_models": 3,
+      "architecture_id": "InternLM3ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "HuggingFaceM4/idefics-80b-instruct",
-        "HuggingFaceM4/idefics-9b",
-        "HuggingFaceM4/idefics-9b-instruct"
+        "internlm/internlm3-8b-instruct"
       ]
     },
     {
-      "architecture_id": "OLMoForCausalLM",
-      "total_models": 3,
+      "architecture_id": "GritLM",
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       "sample_models": [
-        "allenai/OLMo-1B",
-        "allenai/OLMo-7B-Instruct",
-        "allenai/OLMo-7B"
+        "parasail-ai/GritLM-7B-vllm"
       ]
     },
     {
-      "architecture_id": "modeling_camelidae.LlamaForCausalLM",
-      "total_models": 3,
+      "architecture_id": "ExaoneMoEForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "hywu/Camelidae-8x34B",
-        "hywu/Camelidae-8x13B",
-        "hywu/Camelidae-8x7B"
+        "LGAI-EXAONE/K-EXAONE-236B-A23B"
       ]
     },
     {
-      "architecture_id": "LISAForCausalLM",
-      "total_models": 3,
+      "architecture_id": "BailingMoeV2_5ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "xinlai/LISA-13B-llama2-v1",
-        "xinlai/LISA-7B-v1",
-        "MBZUAI/GLaMM-GranD-Pretrained"
+        "inclusionAI/Ring-2.5-1T"
       ]
     },
     {
-      "architecture_id": "RWKV7ForCausalLM",
-      "total_models": 3,
+      "architecture_id": "Grok1ModelForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "RWKV/RWKV7-Goose-World3-1.5B-HF",
-        "fla-hub/rwkv7-1.5B-world",
-        "RWKV/RWKV7-Goose-World3-2.9B-HF"
+        "hpcai-tech/grok-1"
       ]
     },
     {
-      "architecture_id": "MotifForCausalLM",
-      "total_models": 3,
+      "architecture_id": "BailingMoeForCausalLM",
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       "sample_models": [
-        "Motif-Technologies/Motif-2-12.7B-Base",
-        "Motif-Technologies/Motif-2-12.7B-Instruct",
-        "Motif-Technologies/Motif-2.6b-v1.1-LC"
+        "inclusionAI/Ling-lite-1.5"
       ]
     },
     {
-      "architecture_id": "H2OVLChatModel",
-      "total_models": 2,
+      "architecture_id": "SolarForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "h2oai/h2ovl-mississippi-800m",
-        "h2oai/h2ovl-mississippi-2b"
+        "upstage/solar-pro-preview-instruct"
       ]
     },
     {
-      "architecture_id": "KimiK25ForConditionalGeneration",
-      "total_models": 2,
+      "architecture_id": "HunYuanMoEV1ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "nvidia/Kimi-K2.5-NVFP4",
-        "Ex0bit/Kimi-K2.5-PRISM-REAP-530B-A32B"
+        "tencent/Hunyuan-A13B-Instruct"
       ]
     },
     {
-      "architecture_id": "HCXVisionV2ForCausalLM",
-      "total_models": 2,
+      "architecture_id": "GptOssPuzzleForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "naver-hyperclovax/HyperCLOVAX-SEED-Omni-8B",
-        "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B"
+        "nvidia/gpt-oss-puzzle-88B"
       ]
     },
     {
-      "architecture_id": "OpenAIGPTLMHeadModel",
-      "total_models": 2,
+      "architecture_id": "MiniMaxForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "openai-community/openai-gpt",
-        "lgaalves/gpt1"
+        "MiniMaxAI/MiniMax-Text-01-hf"
       ]
     },
     {
-      "architecture_id": "MoshiForConditionalGeneration",
-      "total_models": 2,
+      "architecture_id": "MiniCPM3ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "kmhf/hf-moshiko",
-        "kmhf/hf-moshika"
+        "openbmb/MiniCPM3-4B"
       ]
     },
     {
-      "architecture_id": "ReformerModelWithLMHead",
-      "total_models": 2,
+      "architecture_id": "ArcticForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "google/reformer-crime-and-punishment",
-        "google/reformer-enwik8"
+        "Snowflake/snowflake-arctic-instruct"
       ]
     },
     {
-      "architecture_id": "Phi3VForCausalLM",
-      "total_models": 2,
+      "architecture_id": "IQuestLoopCoderForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "microsoft/Phi-3-vision-128k-instruct",
-        "TIGER-Lab/VLM2Vec-Full"
+        "IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct"
       ]
     },
     {
-      "architecture_id": "BartForConditionalGeneration",
-      "total_models": 2,
+      "architecture_id": "EchoForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "KomeijiForce/bart-large-emojilm",
-        "Nargizi/screeve-lemmatizer"
+        "ethicalabs/Echo-DSRN-486M-v0.7.6-SFT"
       ]
     },
     {
-      "architecture_id": "StarVectorForCausalLM",
-      "total_models": 2,
+      "architecture_id": "ModernBertForSequenceClassification",
+      "total_models": 1,
       "sample_models": [
-        "starvector/starvector-1b-im2svg",
-        "starvector/starvector-8b-im2svg"
+        "opendatalab/meta-rater-professionalism-rating"
       ]
     },
     {
-      "architecture_id": "KimiLinearForCausalLM",
-      "total_models": 2,
+      "architecture_id": "Plamo3ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "moonshotai/Kimi-Linear-48B-A3B-Instruct",
-        "moonshotai/Kimi-Linear-48B-A3B-Base"
+        "pfnet/plamo-3-nict-2b-base"
       ]
     },
     {
-      "architecture_id": "DbrxForCausalLM",
-      "total_models": 2,
+      "architecture_id": "Dots1ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "trl-internal-testing/tiny-DbrxForCausalLM",
-        "katuni4ka/tiny-random-dbrx"
+        "rednote-hilab/dots.llm1.inst"
       ]
     },
     {
-      "architecture_id": "Qwen2_5_VLForConditionalGeneration",
-      "total_models": 2,
+      "architecture_id": "GraphT5TransformerForConditionalGeneration",
+      "total_models": 1,
       "sample_models": [
-        "nvidia/Qwen2.5-VL-7B-Instruct-NVFP4",
-        "OmniSVG/OmniSVG"
+        "haitengzhao/gimlet"
       ]
     },
     {
-      "architecture_id": "ChatGLMModel",
-      "total_models": 2,
+      "architecture_id": "TransformerForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "zai-org/glm-4-9b",
-        "zai-org/codegeex4-all-9b"
+        "fla-hub/transformer-1.3B-100B"
       ]
     },
     {
-      "architecture_id": "Llama4ForCausalLM",
-      "total_models": 2,
+      "architecture_id": "Moondream",
+      "total_models": 1,
       "sample_models": [
-        "trl-internal-testing/tiny-Llama4ForCausalLM",
-        "facebook/MobileLLM-R1-950M"
+        "vikhyatk/moondream1"
       ]
     },
     {
-      "architecture_id": "Phi3SmallForCausalLM",
-      "total_models": 2,
+      "architecture_id": "GPTRefactForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "microsoft/Phi-3-small-8k-instruct",
-        "microsoft/Phi-3-small-128k-instruct"
+        "refactai/Refact-1_6B-fim"
       ]
     },
     {
-      "architecture_id": "MiniMaxM1ForCausalLM",
-      "total_models": 2,
+      "architecture_id": "GPT2LMHeadCustomModel",
+      "total_models": 1,
       "sample_models": [
-        "MiniMaxAI/MiniMax-M1-40k",
-        "MiniMaxAI/MiniMax-M1-80k"
+        "bigcode/santacoder"
       ]
     },
     {
-      "architecture_id": "CLIPT5ForConditionalGeneration",
-      "total_models": 2,
+      "architecture_id": "TrillionForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "zhiqiulin/clip-flant5-xxl",
-        "zhiqiulin/clip-flant5-xl"
+        "trillionlabs/Tri-21B-Think"
       ]
     },
     {
-      "architecture_id": "BailingMoeForCausalLM",
-      "total_models": 2,
+      "architecture_id": "InternLMXComposer2ForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "inclusionAI/Ling-lite-1.5",
-        "inclusionAI/Ling-lite"
+        "internlm/internlm-xcomposer2-7b"
       ]
     },
     {
-      "architecture_id": "BitNetForCausalLM",
-      "total_models": 2,
+      "architecture_id": "StableLMAlphaForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "microsoft/bitnet-b1.58-2B-4T",
-        "microsoft/bitnet-b1.58-2B-4T-bf16"
+        "stabilityai/stablelm-base-alpha-7b-v2"
       ]
     },
     {
-      "architecture_id": "InternVLChatModel",
-      "total_models": 2,
+      "architecture_id": "NandiForCausalLM",
+      "total_models": 1,
       "sample_models": [
-        "numind/NuExtract-2-4B-experimental",
-        "numind/NuExtract-2-8B-experimental"
+        "Rta-AILabs/Nandi-Mini-150M"
       ]
     },
     {
-      "architecture_id": "Ernie4_5ForCausalLM",
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-        "baidu/ERNIE-4.5-0.3B-Base-PT"
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       ]
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     {
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-        "IQuestLab/IQuest-Coder-V1-40B-Instruct",
-        "IQuestLab/IQuest-Coder-V1-7B-Instruct"
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       ]
     },
     {
-      "architecture_id": "XverseForCausalLM",
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-        "xverse/XVERSE-7B-Chat",
-        "katuni4ka/tiny-random-xverse"
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       ]
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-        "inceptionai/Jais-2-8B-Chat",
-        "inceptionai/Jais-2-70B-Chat"
+        "bytedance-research/ChatTS-8B"
       ]
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     {
-      "architecture_id": "StripedHyenaModelForCausalLM",
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-        "togethercomputer/evo-1-131k-base"
+        "FreedomIntelligence/openPangu-Embedded-1B"
       ]
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-      "architecture_id": "AXK1ForCausalLM",
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-        "skt/A.X-K1",
-        "thkim93/axk1-2layers"
+        "openbmb/NOSA-8B"
       ]
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-        "google/recurrentgemma-2b",
-        "google/recurrentgemma-2b-it"
+        "Hawa-Al-Akram/StarO-Ai"
       ]
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-        "shanearora/Flex-reddit-2x7B-1T"
+        "nyu-visionx/Scale-RAE-Qwen1.5B_DiT2.4B"
       ]
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     {
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-        "nota-ai/Solar-Open-100B-NotaMoEQuant-Int4"
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       ]
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     {
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-        "tencent/Penguin-VL-2B"
+        "numind/NuExtract-2-4B-experimental"
       ]
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-        "ralyn/NPComposer-v2"
+        "google/vaultgemma-1b"
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-        "fla-hub/gla-1.3B-100B"
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-        "anas-awadalla/mpt-1b-redpajama-200b-dolly"
+        "StanfordAIMI/CheXagent-2-3b"
       ]
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-        "RedHatAI/Qwen3-235B-A22B-Instruct-2507-speculator.eagle3"
+        "Zyphra/Zamba-7B-v1"
       ]
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-        "allenai/Bolmo-1B"
+        "deqing/gdn-300M-v5-gdn"
       ]
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-        "jetmoe/jetmoe-8b-chat"
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       ]
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     {
-      "architecture_id": "CogVLMForCausalLM",
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-        "zai-org/cogvlm-chat-hf"
+        "bytedance-research/ChatTS-14B"
       ]
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-      "architecture_id": "WeDLMForCausalLM",
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-        "tencent/WeDLM-8B-Base"
+        "jetmoe/jetmoe-8b"
       ]
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-      "architecture_id": "YoutuForCausalLM",
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-        "tencent/Youtu-LLM-2B"
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       ]
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     {
-      "architecture_id": "ParamBharatGenForCausalLM",
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-        "bharatgenai/AyurParam"
+        "Girinath11/recursive-language-model-198m"
       ]
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-      "architecture_id": "BitnetForCausalLM",
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-        "1bitLLM/bitnet_b1_58-large"
+        "levossadtchi/QED-75M"
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     {
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-        "c-bone/CrystaLLM-pi_COD-XRD"
+        "astanahub/alemllm"
       ]
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     {
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       "sample_models": [
-        "thesephist/contra-bottleneck-t5-base-wikipedia",
-        "thesephist/contra-bottleneck-t5-large-wikipedia"
+        "Prositron/gome"
       ]
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     {
-      "architecture_id": "MptForCausalLM",
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-        "team-lucid/mptk-1b",
-        "explosion-testing/mpt-test"
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       ]
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     {
-      "architecture_id": "OpenMoeForCausalLM",
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-        "hpcai-tech/openmoe-8B",
-        "OrionZheng/openmoe-8b"
+        "RedHatAI/Qwen3-VL-32B-Instruct-NVFP4"
       ]
     },
     {
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-        "XiaomiMiMo/MiMo-V2-Flash"
+        "microsoft/maira-2"
       ]
     },
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-        "XLabs-AI/xflux_text_encoders"
+        "rudyon/linnet-497M"
       ]
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-        "XCurOS/XCurOS-0.1-8B-Instruct"
+        "Madras1/GTLM-1-2B-A350M"
       ]
     },
     {
-      "architecture_id": "GPTNeoXJapaneseForCausalLM",
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       "sample_models": [
-        "abeja/gpt-neox-japanese-2.7b"
+        "dongbobo/MyAwesomeModel"
       ]
     },
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-        "hmellor/Ilama-3.2-1B"
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       ]
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-        "pfnet/plamo-2-1b"
+        "reaperdoesntknow/SAGI"
       ]
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-        "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B"
+        "manycore-research/SpatialLM1.1-Qwen-0.5B"
       ]
     },
     {
-      "architecture_id": "TarsierForConditionalGeneration",
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-        "omni-research/Tarsier-7b"
+        "xiao-fei/Prot2Text-V2-11B-Instruct-hf"
       ]
     },
     {
-      "architecture_id": "BaiChuanForCausalLM",
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-        "baichuan-inc/Baichuan-7B"
+        "nguyenvulebinh/AV-HuBERT-MuAViC-en"
       ]
     },
     {
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-        "sarvamai/sarvam-30b"
+        "sshleifer/tiny-ctrl"
       ]
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-        "meituan-longcat/LongCat-Flash-Chat"
+        "aisingapore/coherence-momentum"
       ]
     },
     {
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       "sample_models": [
-        "refactai/Refact-1_6B-fim"
+        "reaperdoesntknow/TameForCasualLM"
       ]
     },
     {
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-        "naver-hyperclovax/HyperCLOVAX-SEED-Think-14B"
+        "fxmarty/tiny-testing-gpt2-remote-code"
       ]
     },
     {
-      "architecture_id": "ExaoneMoEForCausalLM",
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-        "LGAI-EXAONE/K-EXAONE-236B-A23B"
+        "NamrataThakur/Small_Language_Model_MHA_53M_Pretrained"
       ]
     },
     {
-      "architecture_id": "HunYuanMoEV1ForCausalLM",
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-        "tencent/Hunyuan-A13B-Instruct"
+        "NamrataThakur/Small_Language_Model_GQA_48M_Pretrained"
       ]
     },
     {
-      "architecture_id": "GritLM",
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-        "parasail-ai/GritLM-7B-vllm"
+        "NamrataThakur/Small_Language_Model_MOE_127M_Pretrained"
       ]
     },
     {
-      "architecture_id": "BailingMoeV2_5ForCausalLM",
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-        "inclusionAI/Ring-2.5-1T"
+        "mtgv/MobileVLM_V2-1.7B"
       ]
     },
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-      "architecture_id": "SolarForCausalLM",
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       "sample_models": [
-        "upstage/solar-pro-preview-instruct"
+        "Manhph2211/Q-HEART"
       ]
     },
     {
-      "architecture_id": "JetNemotronForCausalLM",
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-        "jet-ai/Jet-Nemotron-2B"
+        "KKHYA/llavaqwen2.5-0.5b-finetune-moe-4e-2k_20260331_194516"
       ]
     },
     {
-      "architecture_id": "InternLM3ForCausalLM",
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-        "internlm/internlm3-8b-instruct"
+        "prskid1000/micro-Omni"
       ]
     },
     {
-      "architecture_id": "Grok1ModelForCausalLM",
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       "sample_models": [
-        "hpcai-tech/grok-1"
+        "bhsinghgrid/sanskrit-translation"
       ]
     },
     {
-      "architecture_id": "Qwen3VLMoeForConditionalGeneration",
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       "total_models": 1,
       "sample_models": [
-        "RedHatAI/Qwen3-VL-235B-A22B-Instruct-NVFP4"
+        "nvidia/Kimi-K2.5-Thinking-Eagle3"
       ]
     },
     {
-      "architecture_id": "MiniCPM3ForCausalLM",
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       "total_models": 1,
       "sample_models": [
-        "openbmb/MiniCPM3-4B"
+        "evilfreelancer/ruGPT3XL"
       ]
     },
     {
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+      "architecture_id": "Videollama2Qwen2ForCausalLM",
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       "sample_models": [
-        "BAAI/Emu3-Chat"
+        "QuangTuan/MultiMood-7B-GRPO-VisualAudioText-Comp"
       ]
     },
     {
-      "architecture_id": "GRIN-MoE",
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       "total_models": 1,
       "sample_models": [
-        "microsoft/GRIN-MoE"
+        "valhalla/s2t_mustc_multilinguial_medium"
       ]
     },
     {
-      "architecture_id": "AV2TextForConditionalGeneration",
+      "architecture_id": "Autoencoder",
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-        "nguyenvulebinh/AV-HuBERT-MuAViC-en"
+        "cccczshao/CALM-Autoencoder"
       ]
     },
     {
-      "architecture_id": "MiniMaxForCausalLM",
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       "sample_models": [
-        "MiniMaxAI/MiniMax-Text-01-hf"
+        "thu-coai/blenderbot-400M-esconv"
       ]
     },
     {
-      "architecture_id": "ArcticForCausalLM",
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       "sample_models": [
-        "Snowflake/snowflake-arctic-instruct"
+        "wago5090/mixstyle_multi-s"
       ]
     },
     {
-      "architecture_id": "OrionForCausalLM",
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-        "OrionStarAI/Orion-14B-Chat"
+        "xihc-ucb/Qwen3-8B-Base-train-Quasar-0809"
       ]
     },
     {
-      "architecture_id": "IQuestLoopCoderForCausalLM",
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       "sample_models": [
-        "IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct"
+        "EngineerGL/Alinlight"
       ]
     },
     {
-      "architecture_id": "Moondream",
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       "sample_models": [
-        "vikhyatk/moondream1"
+        "thunlp/LLaMA3-Instruct-8B-FR-Spec"
       ]
     },
     {
-      "architecture_id": "SarvamMLAForCausalLM",
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       "sample_models": [
-        "sarvamai/sarvam-105b"
+        "anthonym21/Eve-2-MoE-IT-272M"
       ]
     },
     {
-      "architecture_id": "Plamo3ForCausalLM",
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       "sample_models": [
-        "pfnet/plamo-3-nict-2b-base"
+        "Intel/fid_flan_t5_base_nq"
       ]
     },
     {
-      "architecture_id": "InternLMXComposer2ForCausalLM",
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-        "internlm/internlm-xcomposer2-7b"
+        "GeoV/GeoV-9b"
       ]
     },
     {
-      "architecture_id": "GraphT5TransformerForConditionalGeneration",
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       "sample_models": [
-        "haitengzhao/gimlet"
+        "circulus/alpaca-7b"
       ]
     },
     {
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       "sample_models": [
-        "internlm/internlm-xcomposer-7b"
+        "kgrabko/JiRackTernary_1b"
       ]
     },
     {
-      "architecture_id": "Dots1ForCausalLM",
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       "sample_models": [
-        "rednote-hilab/dots.llm1.inst"
+        "hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM"
       ]
     },
     {
-      "architecture_id": "LlavaSearchLlamaForCausalLM",
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       "sample_models": [
-        "craigwu/seal_vqa_7b"
+        "CofeAI/Tele-FLM-1T"
       ]
     },
     {
-      "architecture_id": "CheXagentForCausalLM",
+      "architecture_id": "Typhoon2Audio2AudioForConditionalGeneration",
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       "sample_models": [
-        "StanfordAIMI/CheXagent-2-3b"
+        "typhoon-ai/llama3.1-typhoon2-audio-8b-instruct"
       ]
     },
     {
-      "architecture_id": "TransfoXLLMHeadModel",
+      "architecture_id": "DenseLLM",
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       "sample_models": [
-        "transfo-xl/transfo-xl-wt103"
+        "AlgoDriveAI/Sanskrit_Akkadian_LLM_v1.0"
       ]
     },
     {
-      "architecture_id": "ZetaGrid25B",
+      "architecture_id": "PegasusForCausalLM",
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       "sample_models": [
-        "RthItalia/Rth-lm-code-25b"
+        "hf-tiny-model-private/tiny-random-PegasusForCausalLM"
       ]
     },
     {
-      "architecture_id": "TransformerForCausalLM",
+      "architecture_id": "BlenderbotForCausalLM",
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       "sample_models": [
-        "fla-hub/transformer-1.3B-100B"
+        "hf-tiny-model-private/tiny-random-BlenderbotForCausalLM"
       ]
     },
     {
-      "architecture_id": "Qwen3VLForConditionalGeneration",
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       "total_models": 1,
       "sample_models": [
-        "RedHatAI/Qwen3-VL-32B-Instruct-NVFP4"
+        "daslab-testing/CloverLM"
       ]
     },
     {
-      "architecture_id": "Rwkv6ForCausalLM",
+      "architecture_id": "GPTModelForTextGeneration",
       "total_models": 1,
       "sample_models": [
-        "RWKV/v6-Finch-1B6-HF"
+        "samkeet/GPT_124M-Instruct"
       ]
     },
     {
-      "architecture_id": "CambrianQwenForCausalLM",
+      "architecture_id": "TFGPT2LMHeadModel",
       "total_models": 1,
       "sample_models": [
-        "nyu-visionx/Scale-RAE-Qwen1.5B_DiT2.4B"
+        "mymusise/gpt2-medium-chinese"
       ]
     },
     {
-      "architecture_id": "VaultGemmaForCausalLM",
+      "architecture_id": "SongGenMixedForConditionalGeneration",
       "total_models": 1,
       "sample_models": [
-        "google/vaultgemma-1b"
+        "LiuZH-19/SongGen_mixed_pro"
       ]
     },
     {
-      "architecture_id": "FP8Qwen2ForCausalLM",
+      "architecture_id": "LIMEForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "xihc-ucb/Qwen2.5-7B-train-Quasar-1214"
+        "anarlavrenov/lime-1b-instruct"
       ]
     },
     {
-      "architecture_id": "SparseLlamaForCausalLM",
+      "architecture_id": "ElectraForCausalLM",
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       "sample_models": [
-        "openbmb/NOSA-8B"
+        "smeoni/nbme-electra-large-generator"
       ]
     },
     {
-      "architecture_id": "SpatialLMQwenForCausalLM",
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       "total_models": 1,
       "sample_models": [
-        "manycore-research/SpatialLM1.1-Qwen-0.5B"
+        "xihc-ucb/Qwen2.5-7B-train-Quasar-1214"
       ]
     },
     {
-      "architecture_id": "VSMForCausalLM",
+      "architecture_id": "LSTMForCausalLM",
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       "sample_models": [
-        "craigwu/seal_vsm_7b"
+        "deqing/lstm-window-4-v5"
       ]
     },
     {
-      "architecture_id": "GPT2LMHeadCustomModel",
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       "sample_models": [
-        "bigcode/santacoder"
+        "hf-tiny-model-private/tiny-random-MvpForCausalLM"
       ]
     },
     {
-      "architecture_id": "MoYiForCausalLM",
+      "architecture_id": "ModernBertForMaskedLM",
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       "sample_models": [
-        "astanahub/alemllm"
+        "JorgeVanco/diffusionGPT"
       ]
     },
     {
-      "architecture_id": "SeerAttnLlamaForCausalLM",
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       "sample_models": [
-        "SeerAttention/SeerAttention-Llama-3.1-8B-AttnGates"
+        "cccczshao/CALM-M"
       ]
     },
     {
-      "architecture_id": "RavenForCausalLM",
+      "architecture_id": "XModelForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "tomg-group-umd/huginn-0125"
+        "XiaoduoAILab/Xmodel_LM"
       ]
     },
     {
-      "architecture_id": "GeoChatLlamaForCausalLM",
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       "sample_models": [
-        "MBZUAI/geochat-7B"
+        "entropy/roberta_zinc_decoder"
       ]
     },
     {
-      "architecture_id": "Param2MoEForCausalLM",
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       "sample_models": [
-        "bharatgenai/Param2-17B-A2.4B-Thinking"
+        "StanfordAIMI/CheXagent-8b"
       ]
     },
     {
-      "architecture_id": "AprielForCausalLM",
+      "architecture_id": "DebertaV2ForCausalLM",
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       "sample_models": [
-        "ServiceNow-AI/Apriel-5B-Instruct"
+        "ltg/deberta-xxlarge-fixed"
       ]
     },
     {
-      "architecture_id": "PanguEmbeddedForCausalLM",
+      "architecture_id": "MoEGPTForCausalLM",
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       "sample_models": [
-        "FreedomIntelligence/openPangu-Embedded-1B"
+        "arnomatic/german-moe-gpt-v8-pretrained"
       ]
     },
     {
-      "architecture_id": "Phi4MMForCausalLM",
+      "architecture_id": "GPTXForCausalLM",
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       "sample_models": [
-        "Yanis-Gerst/fine_tune"
+        "Datdanboi25/GPT-X-125m-15bt"
       ]
     },
     {
-      "architecture_id": "Maira2ForConditionalGeneration",
+      "architecture_id": "SpectusForConditionalGeneration",
       "total_models": 1,
       "sample_models": [
-        "microsoft/maira-2"
+        "MS-ML/SpecTUS_pretrained_only"
       ]
     },
     {
-      "architecture_id": "MiniCPMSALAForCausalLM",
+      "architecture_id": "OmniASRForConditionalGeneration",
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       "sample_models": [
-        "openbmb/MiniCPM-SALA"
+        "bezzam/omniasr-llm-300m-v2"
       ]
     },
     {
-      "architecture_id": "GiddForDiffusionLM",
+      "architecture_id": "LSGBartForConditionalGeneration",
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       "sample_models": [
-        "dvruette/gidd-unif-3b"
+        "morenolq/LEGIT-BART-LSG-4096"
       ]
     },
     {
-      "architecture_id": "SteerlingForCausalLM",
+      "architecture_id": "MiniMaxText01ForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "guidelabs/steerling-8b"
+        "MiniMaxAI/MiniMax-Text-01"
       ]
     },
     {
-      "architecture_id": "StableLMAlphaForCausalLM",
+      "architecture_id": "Kanana2VecModel",
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       "sample_models": [
-        "stabilityai/stablelm-base-alpha-7b-v2"
+        "kakaocorp/kanana-nano-2.1b-embedding"
       ]
     },
     {
-      "architecture_id": "HGRNBitForCausalLM",
+      "architecture_id": "MatriochkaForCausalLM",
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       "sample_models": [
-        "ridger/MMfreeLM-370M"
+        "nthngdy/matryoshka-3B"
       ]
     },
     {
-      "architecture_id": "CheXagentForConditionalGeneration",
+      "architecture_id": "Qwen2VLAudioForConditionalGeneration",
       "total_models": 1,
       "sample_models": [
-        "StanfordAIMI/CheXagent-8b"
+        "MayaKD/qwen2-vl-audio"
       ]
     },
     {
-      "architecture_id": "MiniMaxText01ForCausalLM",
+      "architecture_id": "GuppyLM",
       "total_models": 1,
       "sample_models": [
-        "MiniMaxAI/MiniMax-Text-01"
+        "arman-bd/guppylm-9M"
       ]
     },
     {
-      "architecture_id": "LamedPhi3ForCausalLM",
+      "architecture_id": "Bagel",
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-        "GoodBaiBai88/M3D-LaMed-Phi-3-4B"
+        "lmms-lab/BAGEL-7B-MoT-ver.LE"
       ]
     },
     {
-      "architecture_id": "TorchMultiOmicsModel",
+      "architecture_id": "GPTJXMoEForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "InstaDeepAI/ChatNT"
+        "Aletheia-ng/SabiYarn_MoE_translate"
       ]
     },
     {
-      "architecture_id": "MobileLlamaForCausalLM",
+      "architecture_id": "SliderGPT",
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       "sample_models": [
-        "mtgv/MobileVLM_V2-1.7B"
+        "c-bone/CrystaLLM-pi_Mattergen-XRD"
       ]
     },
     {
-      "architecture_id": "Phi4FlashForCausalLM",
+      "architecture_id": "LatentMoELLaVAPhiForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "microsoft/Phi-4-mini-flash-reasoning"
+        "KKHYA/llavaphi2-2.7b-finetune-latent-sparse-moe-4e-2k-freeze-1.0_20260304_075653"
       ]
     },
     {
-      "architecture_id": "DeciCoderForCausalLM",
+      "architecture_id": "LlavaCrystalForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "Deci/DeciCoder-1b"
+        "LLM360/CrystalChat-7B-Web2Code"
       ]
     },
     {
-      "architecture_id": "GPT3DevLMHeadModel",
+      "architecture_id": "MobileLLMP1ForCausalLM",
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       "sample_models": [
-        "k050506koch/GPT3-dev-350m-2805"
+        "facebook/MobileLLM-Pro-base"
       ]
     },
     {
-      "architecture_id": "Qwen2VLForConditionalGeneration",
+      "architecture_id": "CircuitGPTForCausalLM",
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       "sample_models": [
-        "yujiepan/qwen2-vl-tiny-random"
+        "openai/circuit-sparsity"
       ]
     },
     {
-      "architecture_id": "Qwen2ForSequenceClassification",
+      "architecture_id": "GeoChatLlamaForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "nvidia/AceMath-7B-RM"
+        "MBZUAI/geochat-7B"
       ]
     },
     {
-      "architecture_id": "Kanana2VecModel",
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       "sample_models": [
-        "kakaocorp/kanana-nano-2.1b-embedding"
+        "facebook/MobileLLM-125M"
       ]
     },
     {
-      "architecture_id": "EchoForCausalLM",
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       "sample_models": [
-        "ethicalabs/Echo-DSRN-486M-v0.7.6-SFT"
+        "dvruette/gidd-unif-3b"
       ]
     },
     {
-      "architecture_id": "CTRLLMHeadModel",
+      "architecture_id": "InternLM2ForRewardModel",
       "total_models": 1,
       "sample_models": [
-        "sshleifer/tiny-ctrl"
+        "internlm/internlm2_5-step-prover-critic"
       ]
     },
     {
-      "architecture_id": "LLaDAMoEModel",
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       "sample_models": [
-        "inclusionAI/LLaDA-MoE-7B-A1B-Base"
+        "JeffGreen311/Eve-V2-Unleashed-Qwen3.5-8B-Liberated-4K-4B-Merged"
       ]
     },
     {
-      "architecture_id": "CPMAntForCausalLM",
+      "architecture_id": "MiniCPMSALAForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "openbmb/cpm-ant-10b"
+        "openbmb/MiniCPM-SALA"
       ]
     },
     {
-      "architecture_id": "ICONNForCausalLM",
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       "sample_models": [
-        "ICONNAI/ICONN-1-Mini-Beta"
+        "GoodBaiBai88/M3D-LaMed-Phi-3-4B"
       ]
     },
     {
-      "architecture_id": "HeliumForCausalLM",
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       "sample_models": [
-        "kyutai/helium-1-preview-2b"
+        "KORMo-Team/KORMo-10B-sft"
       ]
     },
     {
-      "architecture_id": "DogeForCausalLM",
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       "sample_models": [
-        "SmallDoge/Doge-20M"
+        "Deci/DeciCoder-1b"
       ]
     },
     {
-      "architecture_id": "LongcatFlashNgramForCausalLM",
+      "architecture_id": "AeroForConditionalGeneration",
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       "sample_models": [
-        "meituan-longcat/LongCat-Flash-Lite"
+        "lmms-lab/Aero-1-Audio"
       ]
     },
     {
-      "architecture_id": "GPT",
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       "sample_models": [
-        "LH-Tech-AI/Apex-1.5-Coder-Instruct-350M"
+        "kyutai/helium-1-preview-2b"
       ]
     },
     {
-      "architecture_id": "GPT2CustomLMHeadModel",
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       "sample_models": [
-        "fxmarty/tiny-testing-gpt2-remote-code"
+        "mobilint/EXAONE-4.0-1.2B"
       ]
     },
     {
-      "architecture_id": "SKTOmniForConditionalGeneration",
+      "architecture_id": "BD3LM",
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       "sample_models": [
-        "Shrijanagain/SKT_OMNI_SUPREME"
+        "kuleshov-group/bd3lm-owt-block_size4"
       ]
     },
     {
-      "architecture_id": "MobileLLMForCausalLM",
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-        "facebook/MobileLLM-125M"
+        "DAMO-NLP-MT/polylm-13b"
       ]
     },
     {
-      "architecture_id": "CircuitGPTForCausalLM",
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-        "openai/circuit-sparsity"
+        "typhoon-ai/llama-3-typhoon-v1.5-8b-vision-preview"
       ]
     },
     {
-      "architecture_id": "Qwen3TSForCausalLM",
+      "architecture_id": "Emu3ForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "bytedance-research/ChatTS-8B"
+        "BAAI/Emu3-Chat"
       ]
     },
     {
-      "architecture_id": "ConditionalGPT",
+      "architecture_id": "JiRackTernaryModel",
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       "sample_models": [
-        "c-bone/CrystaLLM-pi_bandgap"
+        "kgrabko/JiRackTernary_70b"
       ]
     },
     {
-      "architecture_id": "DuchifatCore",
+      "architecture_id": "SKTOmniForConditionalGeneration",
       "total_models": 1,
       "sample_models": [
-        "Raziel1234/Duchifat-2"
+        "Shrijanagain/SKT_OMNI_SUPREME"
       ]
     },
     {
-      "architecture_id": "GPT2Model",
+      "architecture_id": "CambrianLlamaForCausalLM",
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       "sample_models": [
-        "cerebras/Cerebras-GPT-13B"
+        "nyu-visionx/cambrian-8b"
       ]
     },
     {
-      "architecture_id": "BD3LM",
+      "architecture_id": "OpenMoeForCausalLM",
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       "sample_models": [
-        "kuleshov-group/bd3lm-owt-block_size4"
+        "hpcai-tech/openmoe-8B"
       ]
     },
     {
-      "architecture_id": "AeroForConditionalGeneration",
+      "architecture_id": "LlamaModel",
       "total_models": 1,
       "sample_models": [
-        "lmms-lab/Aero-1-Audio"
+        "ngoan/NgoanYi"
       ]
     },
     {
-      "architecture_id": "KORMoForCausalLM",
+      "architecture_id": "DUO",
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       "sample_models": [
-        "KORMo-Team/KORMo-10B-sft"
+        "s-sahoo/duo-distilled"
       ]
     },
     {
-      "architecture_id": "PhariaForCausalLM",
+      "architecture_id": "SteerlingForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "Aleph-Alpha/Pharia-1-LLM-7B-control-hf"
+        "guidelabs/steerling-8b"
       ]
     },
     {
-      "architecture_id": "UMT5ForConditionalGeneration",
+      "architecture_id": "TransnormerForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "EleutherAI/pile-t5-xl"
+        "OpenNLPLab/TransNormerLLM-385M"
       ]
     },
     {
-      "architecture_id": "ZambaForCausalLM",
+      "architecture_id": "RWKV7ForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "Zyphra/Zamba-7B-v1"
+        "RWKV/RWKV7-Goose-World3-1.5B-HF"
       ]
     },
     {
-      "architecture_id": "PolyLMHeadModel",
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       "sample_models": [
-        "DAMO-NLP-MT/polylm-13b"
+        "ICONNAI/ICONN-1-Mini-Beta"
       ]
     },
     {
-      "architecture_id": "RecursiveLanguageModel",
+      "architecture_id": "ErnieForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "Girinath11/recursive-language-model-198m"
+        "mohitsha/tiny-ernie-random-remote-code"
       ]
     },
     {
-      "architecture_id": "SpatialLMLlamaForCausalLM",
+      "architecture_id": "SoraForSLM",
       "total_models": 1,
       "sample_models": [
-        "manycore-research/SpatialLM1.1-Llama-1B"
+        "Conlanger-LLM-CLEM/Sorie"
       ]
     },
     {
-      "architecture_id": "PointLLMLlamaForCausalLM",
+      "architecture_id": "ShikraLlamaForCausalLM",
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       "sample_models": [
-        "RunsenXu/PointLLM_7B_v1.2"
+        "shikras/shikra-7b-delta-v1"
       ]
     },
     {
-      "architecture_id": "MegaForCausalLM",
+      "architecture_id": "YiForCausalLM",
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       "sample_models": [
-        "BEE-spoke-data/mega-ar-126m-4k"
+        "llmware/dragon-yi-6b-v0"
       ]
     },
     {
-      "architecture_id": "SongGenMixedForConditionalGeneration",
+      "architecture_id": "BottleneckT5LMWithPerturb",
       "total_models": 1,
       "sample_models": [
-        "LiuZH-19/SongGen_mixed_pro"
+        "thesephist/contra-bottleneck-t5-small-wikipedia"
       ]
     },
     {
-      "architecture_id": "DUO",
+      "architecture_id": "Phi4FlashForCausalLM",
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       "sample_models": [
-        "s-sahoo/duo-distilled"
+        "microsoft/Phi-4-mini-flash-reasoning"
       ]
     },
     {
-      "architecture_id": "LlamaModel",
+      "architecture_id": "CpmBeeForCausalLM",
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       "sample_models": [
-        "ngoan/NgoanYi"
+        "openbmb/cpm-bee-10b"
       ]
     },
     {
-      "architecture_id": "BailingMoeLinearV2ForCausalLM",
+      "architecture_id": "ZsGPT2LMHeadModel",
       "total_models": 1,
       "sample_models": [
-        "inclusionAI/Ring-mini-linear-2.0"
+        "claritylab/zero-shot-vanilla-gpt2"
       ]
     },
     {
-      "architecture_id": "BertLMHeadModel",
+      "architecture_id": "DotLMForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "dicta-il/BEREL_3.0"
+        "tensorfiend/DotLM-165M"
       ]
     },
     {
-      "architecture_id": "Glm4MoeLiteSonicForCausalLM",
+      "architecture_id": "MiniMindForCausalLM",
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       "sample_models": [
-        "rpDungeon/GLM-4.7-Flash-SonicMOE"
+        "yiwenX/MiniMind-MoE-640-120M"
       ]
     },
     {
-      "architecture_id": "Bagel",
+      "architecture_id": "HumanGPTForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "lmms-lab/BAGEL-7B-MoT-ver.LE"
+        "YaoFeng/CHATPOSE-V0"
       ]
     },
     {
-      "architecture_id": "GLaMMForCausalLM",
+      "architecture_id": "HGRNBitForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "MBZUAI/GLaMM-FullScope"
+        "ridger/MMfreeLM-370M"
       ]
     },
     {
-      "architecture_id": "KonkanGPT",
+      "architecture_id": "Qwen2VLForConditionalGeneration",
       "total_models": 1,
       "sample_models": [
-        "omdeep22/Gonyai-v1"
+        "typhoon-ai/typhoon2-qwen2vl-7b-vision-instruct"
       ]
     },
     {
-      "architecture_id": "Qwen3OmniMoeThinkerForConditionalGeneration",
+      "architecture_id": "BTLMLMHeadModel",
       "total_models": 1,
       "sample_models": [
-        "ngqtrung/Qwen3-Omni-Thinker-30B-Instruct"
+        "cerebras/btlm-3b-8k-base"
       ]
     },
     {
-      "architecture_id": "C3QwenForCausalLM",
+      "architecture_id": "XMistralForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "liufanfanlff/C3-Context-Cascade-Compression"
+        "Hannibal046/xrag-7b"
       ]
     },
     {
-      "architecture_id": "MonoidForCausalLM",
+      "architecture_id": "OtterForConditionalGeneration",
       "total_models": 1,
       "sample_models": [
-        "NoesisLab/Spartacus-1B-Instruct"
+        "luodian/OTTER-Video-LLaMA7B-DenseCaption"
       ]
     },
     {
-      "architecture_id": "ErnieForCausalLM",
+      "architecture_id": "StableDiffcoderForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "mohitsha/tiny-ernie-random-remote-code"
+        "ByteDance-Seed/Stable-DiffCoder-8B-Base"
       ]
     },
     {
-      "architecture_id": "TransnormerForCausalLM",
+      "architecture_id": "MonkeyLMHeadModel",
       "total_models": 1,
       "sample_models": [
-        "OpenNLPLab/TransNormerLLM-385M"
+        "echo840/Monkey-Chat"
       ]
     },
     {
-      "architecture_id": "PKVGPT",
+      "architecture_id": "Lfm2Prototype1ForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "c-bone/CrystaLLM-pi_SLME"
+        "nntsuzu/LFM2-SFT-Prototype01-1.2B-JP"
       ]
     },
     {
-      "architecture_id": "MedHemoModel",
+      "architecture_id": "TelechatForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "amewebstudio/medhemo-earcp"
+        "Tele-AI/telechat-7B"
       ]
     },
     {
-      "architecture_id": "OpenLMForCausalLM",
+      "architecture_id": "FlamingoForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "nick11roberts/SL-discrep-chinchilla-rw-params5M_maxstep760-flop_1_25e16_step_767"
+        "babylm/flamingo-2024"
       ]
     },
     {
-      "architecture_id": "MCGPTForCausalLM",
+      "architecture_id": "IndexForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "TopAI-1/MCGPT-1"
+        "IndexTeam/Index-1.9B-Chat"
       ]
     },
     {
-      "architecture_id": "HymbaForCausalLM",
+      "architecture_id": "PointLLMLlamaForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "nvidia/Hymba-1.5B-Instruct"
+        "RunsenXu/PointLLM_7B_v1.1_init"
       ]
     },
     {
-      "architecture_id": "LlamaMoEForCausalLM",
+      "architecture_id": "CogVLMVideoForCausalLM",
       "total_models": 1,
       "sample_models": [
-        "llama-moe/LLaMA-MoE-v1-3_5B-2_8"
+        "zai-org/VisionReward-Video"
       ]
     }
   ]
diff --git a/transformer_lens/tools/model_registry/data/supported_models.json b/transformer_lens/tools/model_registry/data/supported_models.json
index fa837711d..2bf7aed3c 100644
--- a/transformer_lens/tools/model_registry/data/supported_models.json
+++ b/transformer_lens/tools/model_registry/data/supported_models.json
@@ -1,14 +1,14 @@
 {
-  "generated_at": "2026-03-19",
+  "generated_at": "2026-04-08",
   "scan_info": {
-    "total_scanned": 3517,
+    "total_scanned": 5001,
     "task_filter": "text-generation",
-    "min_downloads": 500,
-    "scan_duration_seconds": 2.7
+    "min_downloads": 100,
+    "scan_duration_seconds": 2.8
   },
-  "total_architectures": 35,
-  "total_models": 5833,
-  "total_verified": 688,
+  "total_architectures": 36,
+  "total_models": 7237,
+  "total_verified": 690,
   "models": [
     {
       "architecture_id": "Qwen2ForCausalLM",
@@ -42,7 +42,7 @@
       "architecture_id": "GPT2LMHeadModel",
       "model_id": "openai-community/gpt2",
       "status": 1,
-      "verified_date": "2026-04-07",
+      "verified_date": "2026-04-08",
       "metadata": null,
       "note": "Full verification completed",
       "phase1_score": 100.0,
@@ -74204,6 +74204,19662 @@
       "phase1_score": null,
       "phase2_score": null,
       "phase3_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "deepseek-ai/DeepSeek-R1",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-8.3B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "deepseek-ai/DeepSeek-R1-0528",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-1B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-1.7B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-0.6B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "deepseek-ai/DeepSeek-V3",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "nvidia/DeepSeek-R1-0528-NVFP4-v2",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-270M-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "deepseek-ai/DeepSeek-V3-0324",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-8.3B-Safety",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForConditionalGeneration",
+      "model_id": "farbodtavakkoli/OTel-LLM-4B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-8.2B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForConditionalGeneration",
+      "model_id": "farbodtavakkoli/OTel-LLM-12B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "ai-sage/GigaChat3-10B-A1.8B",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 619.0 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Olmo3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-7B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "deepseek-ai/DeepSeek-V3.1",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "moonshotai/Kimi-K2-Instruct",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Config unavailable: The repository moonshotai/Kimi-K2-Instruct contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/moonshotai/Kimi-K2-Instruct ",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "status": 3,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 2/12 components failed (2 critical)",
+      "phase1_score": 50.0,
+      "phase2_score": 100.0,
+      "phase3_score": 94.7,
+      "phase4_score": 70.3,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528",
+      "status": 3,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/32 components failed (18 critical)",
+      "phase1_score": 50.0,
+      "phase2_score": 100.0,
+      "phase3_score": 94.7,
+      "phase4_score": 70.1,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "moonshotai/Moonlight-16B-A3B-Instruct",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 1074.3 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "moonshotai/Kimi-K2-Instruct-0905",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Config unavailable: The repository moonshotai/Kimi-K2-Instruct-0905 contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/moonshotai/Kimi-K2-Inst",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-14B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "GptOssForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-20B-Reasoning",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Gemma3ForConditionalGeneration",
+      "model_id": "farbodtavakkoli/OTel-LLM-27B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Olmo3ForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-32B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "GptOssForCausalLM",
+      "model_id": "farbodtavakkoli/OTel-LLM-20B-IT",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "nvidia/DeepSeek-V3-0324-NVFP4",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "moonshotai/Kimi-K2-Thinking",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Config unavailable: The repository moonshotai/Kimi-K2-Thinking contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/moonshotai/Kimi-K2-Thinking ",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "moonshotai/Moonlight-16B-A3B",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 1074.3 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "unsloth/DeepSeek-V3-0324-BF16",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "prism-ml/Bonsai-8B-mlx-1bit",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "ByteDance-Seed/academic-ds-9B",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 619.2 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "Qwen2ForCausalLM",
+      "model_id": "Virtue-AI-HUB/VulnLLM-R-7B",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "unsloth/DeepSeek-V3.1-Base-BF16",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "Rakuten/RakutenAI-3.0",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
+      "model_id": "OpenDFM/ChemDFM-v1.0-13B",
+      "status": 0,
+      "verified_date": null,
+      "metadata": null,
+      "note": null,
+      "phase1_score": null,
+      "phase2_score": null,
+      "phase3_score": null,
+      "phase4_score": null,
+      "phase7_score": null,
+      "phase8_score": null
+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "model_id": "unsloth/DeepSeek-V3",
+      "status": 2,
+      "verified_date": "2026-04-08",
+      "metadata": null,
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+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
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+    },
+    {
+      "architecture_id": "GPT2LMHeadModel",
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+    },
+    {
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+    },
+    {
+      "architecture_id": "MistralForCausalLM",
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+    },
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+    },
+    {
+      "architecture_id": "Qwen2ForCausalLM",
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+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
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+    },
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+    },
+    {
+      "architecture_id": "Qwen3ForCausalLM",
+      "model_id": "kairawal/Qwen3-32B-EL-SynthDolly-1A",
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+    },
+    {
+      "architecture_id": "OPTForCausalLM",
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+    },
+    {
+      "architecture_id": "OPTForCausalLM",
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+    },
+    {
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+    },
+    {
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+    },
+    {
+      "architecture_id": "DeepseekV3ForCausalLM",
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+      "verified_date": "2026-04-08",
+      "metadata": null,
+      "note": "Estimated 55476.6 GB exceeds 96.0 GB limit",
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+    },
+    {
+      "architecture_id": "T5ForConditionalGeneration",
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+    },
+    {
+      "architecture_id": "Qwen2ForCausalLM",
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+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
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+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
+      "model_id": "marysoh/Llama-3.2-1B-Instruct-SFT-Financial-Sentiment",
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+    },
+    {
+      "architecture_id": "Qwen2ForCausalLM",
+      "model_id": "jana-ashraf-ai/python-assistant",
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+    },
+    {
+      "architecture_id": "LlamaForCausalLM",
+      "model_id": "NeverSleep/MiquMaid-v1-70B",
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     }
   ]
 }
diff --git a/transformer_lens/tools/model_registry/data/verification_history.json b/transformer_lens/tools/model_registry/data/verification_history.json
index bc2fde984..1c8cc2053 100644
--- a/transformer_lens/tools/model_registry/data/verification_history.json
+++ b/transformer_lens/tools/model_registry/data/verification_history.json
@@ -1,5 +1,5 @@
 {
-  "last_updated": "2026-04-07T18:56:31.723897",
+  "last_updated": "2026-04-08T12:05:39.889992",
   "records": [
     {
       "model_id": "Macropodus/macbert4mdcspell_v1",
@@ -11140,6 +11140,356 @@
       "notes": "Full verification completed with issues, low text quality",
       "invalidated": false,
       "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 6/32 components failed (6 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 6/32 components failed (6 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 5/32 components failed (4 critical, 1 medium)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 4/32 components failed (4 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "openai-community/gpt2",
+      "architecture_id": "GPT2LMHeadModel",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/32 components failed (18 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: There was a specific connection error when trying to load trl-internal-testing/tiny-DeepseekV3ForCausalL",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "katuni4ka/tiny-random-deepseek-v3",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: There was a specific connection error when trying to load katuni4ka/tiny-random-deepseek-v3:\n(Request ID",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "optimum-intel-internal-testing/tiny-random-deepseek-v3",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=0.0% < 100.0% (failed: load_bridge_unprocessed) \u2014 Failed to load unprocessed TransformerBridge: There was a specific connection error when trying to load optimum-intel-internal-testing/tiny-random-dee",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/32 components failed (18 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/32 components failed (18 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/32 components failed (18 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM-0528",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/32 components failed (18 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 18/32 components failed (18 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "optimum-intel-internal-testing/tiny-random-gigachat3",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 10/28 components failed (10 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "openai-community/gpt2",
+      "architecture_id": "GPT2LMHeadModel",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "optimum-intel-internal-testing/tiny-random-gigachat3",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 4/22 components failed (4 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "optimum-intel-internal-testing/tiny-random-gigachat3",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 4/22 components failed (4 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 10/24 components failed (10 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 10/24 components failed (10 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 10/24 components failed (10 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "openai-community/gpt2",
+      "architecture_id": "GPT2LMHeadModel",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 1/16 components failed (1 medium)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 1/16 components failed (1 medium)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 2/12 components failed (2 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "trl-internal-testing/tiny-DeepseekV3ForCausalLM",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Below threshold: P1=50.0% < 100.0% (failed: all_components) \u2014 2/12 components failed (2 critical)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "optimum-intel-internal-testing/tiny-random-gigachat3",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
+    },
+    {
+      "model_id": "yujiepan/deepseek-v3-tiny-random",
+      "architecture_id": "DeepseekV3ForCausalLM",
+      "verified_date": "2026-04-08",
+      "verified_by": "verify_models",
+      "transformerlens_version": null,
+      "notes": "Full verification completed with issues: P3=89.5% (failed: value_bias_folding, weight_modification)",
+      "invalidated": false,
+      "invalidation_reason": null
     }
   ]
 }