Open-source framework for fine-tuning and evaluating LLMs. It simplifies the process of experimenting with different training configurations and makes it easy to reproduce and share results, supporting features like LoRA, QLoRA, DeepSpeed, PEFT, and multi-GPU setups.
Cross-referenced across 55 tracked directories
#340
Popularity Rank
2 / 55
Listed In
Growing
Adoption Stage
4/14/2023
Created
11,475
GitHub Stars
1,277
Forks
223
Open Issues
3/19/2026
Last Commit
Gaining traction in the ecosystem
Cross-Posting Opportunities
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Score: 100/100
0 dependency vulnerabilities found
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
a lean, efficient, and easy-to-hack codebase to research LLMs.