oumi-ai/oumi

Health v2 · maintenance only

Adoption not part of the score

9373 stars · 784 forks observed · 2026-08-28

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

Oumi is an open-source Python framework and platform for the end-to-end lifecycle of open-weight LLMs: data synthesis, fine-tuning (SFT, LoRA, DPO, GRPO), evaluation, and deployment. It supports models like Qwen, Gemma, Llama, and GPT-OSS, with a CLI, an MCP server, and integrations with vLLM, TRL, and cloud GPU providers.

Use cases

• fine-tune llama on my own dataset
• run sft or dpo training on an open weight llm
• evaluate an llm on a custom evaluation dataset
• deploy a fine-tuned model as an inference endpoint
• synthesize training data with an llm
• train a small language model for a specific task
• distill a frontier model into a smaller one
• run grpo reinforcement learning with tool use

When to choose

• you want one open-source tool covering data, training, evaluation, and deployment for open-weight LLMs
• you need to fine-tune Qwen, Gemma, Llama, or VLMs with SFT, LoRA, DPO, or GRPO
• you want to deploy dedicated inference endpoints or run inference with vLLM
• you prefer Apache-2.0 licensing and Python-based configuration

When to avoid

• you only need prompt-based access to closed frontier models via an API
• you need a no-code GUI-only training experience
• you lack GPU resources and don't want to use cloud training
• you need a lightweight inference-only server without training features

Facets

framework · maturity active

llm-training llm-inference machine-learning rag mcp cli data-generation large-language-models machine-learning deep-learning artificial-intelligence developer-tools python cli cloud fine-tuning sft dpo grpo reinforcement-learning open-weights vlms model-deployment distillation vllm peft lora evaluation gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="oumi-ai/oumi")