a lean, efficient, and easy-to-hack codebase to research LLMs.
Cross-referenced across 55 tracked directories
#2818
Popularity Rank
1 / 55
Listed In
Emerging
Adoption Stage
10/16/2024
Created
4,754
GitHub Stars
270
Forks
11
Open Issues
7/18/2025
Last Commit
Recently added to the ecosystem
Score: 100/100
0 dependency vulnerabilities found
20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.
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.
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