BobaZooba/xllm: 🦖 X—LLM: Cutting Edge & Easy LLM Finetuning

🦖 X—LLM: Cutting Edge & Easy LLM Finetuning. Contribute to BobaZooba/xllm development by creating an account on GitHub.

🦖 X—LLM: Cutting Edge & Easy LLM Finetuning Cutting Edge & Easy LLM Finetuning using the most advanced methods (QLoRA, DeepSpeed, GPTQ, Flash Attention 2, FSDP, etc) Developed by Boris Zubarev | CV | LinkedIn | bobazooba@gmail.com Why you should use X—LLM 🪄 Are you using Large Language Models (LLMs) for your work and want to train them more efficiently with advanced methods? Wish to focus on the data and improvements rather than repetitive and time-consuming coding for LLM training? X—LLM is your solution. It's a user-friendly library that streamlines training optimization, so you can focus

related reading

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• Llama 2 · Hugging Facehuggingface.co
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• Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem – PyTorchpytorch.org
• Fine-tuning a LLM on my blog posts | Didier Lopesdidierlopes.com
• GitHub - openai/parameter-golf: Train the smallest LM you can that fits in 16MB. Best model wins! · GitHubgithub.com
• The Ultra-Scale Playbook - a Hugging Face Space by nanotronhuggingface.co
• Accelerating LLaMA with Fabric: A Comprehensive Guide to Training and Fine-Tuning LLaMA - Lightning AIlightning.ai