Fine-tuningFreeOpen Source

LLM FOUNDRY

Open-source LLM training, fine-tuning, evaluation and deployment framework

Apache-2.0

ABOUT

Training and fine-tuning large language models requires complex distributed infrastructure — setting up multi-GPU training loops, gradient checkpointing, mixed precision, and evaluation pipelines is time-consuming and error-prone. LLM Foundry solves this by providing a complete, production-tested toolkit that handles distributed training, fine-tuning (including LoRA/QLoRA), benchmark evaluation, and model deployment with a clean YAML-based configuration system, letting teams focus on model architecture and data rather than infrastructure.

INSTALL

pip install llm-foundry

INTEGRATION GUIDE

1. Pre-train custom LLMs from scratch using state-of-the-art architectures like MPT and DBRX with Flash Attention and MoE 2. Fine-tune open-source models (Llama, MPT) on domain-specific datasets with LoRA and QLoRA for specialized tasks 3. Evaluate LLMs on academic benchmarks like MMLU, HellaSwag, and Winograd with built-in in-context learning evaluators 4. Convert and deploy fine-tuned models to HuggingFace format or ONNX for production inference serving 5. Experiment with Mixture-of-Experts architectures and efficient attention mechanisms without building infrastructure from scratch

LLM FOUNDRY

ABOUT

INTEGRATION GUIDE

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