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Fine-tuningFreeOpen Source
OUMI
Everything you need to build state-of-the-art foundation models, end-to-end
Apache-2.0
ABOUT
Fine-tuning and deploying foundation models involves a fragmented toolchain — separate scripts for data prep, training, evaluation, and inference across different hardware configurations. Oumi unifies this lifecycle by providing one configuration-driven platform that handles data synthesis, LLM-as-a-judge curation, distributed training (SFT/DPO/GRPO/LoRA), standard benchmarks, and vLLM/SGLang-based deployment — from laptop to multi-node clusters.
INSTALL
pip install oumiINTEGRATION GUIDE
1. Full fine-tuning and LoRA/QLoRA tuning of open-source LLMs like Llama 4 and DeepSeek
2. GRPO/RLHF fine-tuning for improving reasoning model performance
3. Synthesize training data and curate datasets with LLM-as-a-judge evaluation
4. Evaluate models across standard LLM and VLM benchmarks in a consistent pipeline
5. Deploy fine-tuned models with optimized inference engines (vLLM, SGLang) to production
TAGS
fine-tuningsftdpoloraqloragrpotrainingllmevaluationinferencevlmdeepseekllamaqwen