Finetune & Run Llama 3.1 with Unsloth

Meta's Llama 3.1 models, updated to support longer context lengths and new languages, benefit significantly from Unsloth's innovations that improve fine-tuning speed and reduce VRAM usage. Unsloth's enhancements make Llama 3.1 (8B) 2.1 times faster and 60% more memory efficient, while Llama 3.1 (70B) becomes 1.9 times faster with a 65% reduction in VRAM usage. Unsloth's approach allows fine-tuning on larger context lengths, which is particularly advantageous for Llama 3.1 (70B) when comparing its performance with other frameworks like Hugging Face and Flash Attention 2. The introduction of a new chat UI on Google Colab for Llama 3.1 models and the provision of pre-quantized 4-bit models facilitate faster downloads and improved interaction. Additionally, Llama 3.1's architecture now supports training other models using its outputs and employs fp8 precision for efficiency. These advancements underscore the importance of open-source development, as highlighted by Meta's commitment to enabling organizations to tailor models to specific needs without data exposure.