An end-to-end platform for fine-tuning and serving large language models (LLMs) on domain-specific datasets. Built with LoRA/PEFT, distributed training, and production-grade deployment.
Data Pipeline Implementation → For detailed information checkout Data-Pipeline README
Fine-Tuning → Parameter-efficient training (LoRA/QLoRA) on user datasets.
Scalability → Distributed systems for fast training.
Experiment Tracking → MLflow integration with auto-generated model cards.
Serving → FastAPI + Docker/Kubernetes deployment with GPU batching.
Monitoring → Drift detection, performance dashboards, and feedback loops.
Upload dataset → preprocessing & validation.
Fine-tune base model (StarCoderBase, LLaMA, Falcon).
Track experiments and metrics.
Deploy model as an API endpoint.
Monitor → retrain with new data.
FinTech copilots trained on regulatory codebases.
Healthcare assistants fine-tuned on medical knowledge.
Enterprise AI copilots for private code repositories.