mosaicml/llm-foundry

Health v2 · maintenance only

Adoption not part of the score

4441 stars · 590 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LLM Foundry is a PyTorch-based codebase for training, finetuning, evaluating, and deploying large language models from 125M to 70B+ parameters, built on Composer and the MosaicML/Databricks platform. It includes data preparation scripts, training and inference benchmarking tools, and model conversion to HuggingFace or ONNX formats.

Use cases

• finetune an LLM on my own data
• pretrain a large language model on a GPU cluster
• convert a trained LLM to ONNX for inference
• evaluate an LLM on academic in-context learning tasks
• benchmark LLM training throughput and MFU
• prepare text data for LLM pretraining
• profile LLM inference latency and throughput

When to choose

• you want to pretrain or finetune LLMs at scale on GPUs with an efficient, battle-tested codebase
• you are on the Databricks/MosaicML platform and want integrated workload launching
• you need end-to-end LLM workflows: data prep, training, eval, and export

When to avoid

• you only need to run inference with an existing model without training
• you want a lightweight single-GPU hobbyist finetuning tool with minimal setup
• you are not working in the PyTorch/Composer ecosystem

Facets

library · maturity active

llm-training machine-learning deep-learning nlp benchmarking etl large-language-models deep-learning machine-learning gpu-computing python cloud finetuning pytorch mosaicml databricks onnx-export in-context-learning-eval streaming-dataset natural-language-processing gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="mosaicml/llm-foundry")