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š¦„ Fine-tune Llama 3.1 Ultra-Efficiently with @UnslothAI New comprehensive guide about supervised fine-tuning on @huggingface. Over the last year, I've done a lot of fine-tuning and blogging. This guide brings it all together. š Article: https://t.co/LBLseOPjcx https://t.co/TJXwlrYpEE
If you're a visual learner interested in current approaches to LLM optimization, don't miss @MaartenGr's excellent new guide, which unpacks the math behind ā and practical considerations around ā quantization. https://t.co/85uV0GOM0j
So you can actually train a small model to be an expert in a specific domain by leveraging a larger, more capable model to teach it! I made a simple Colab notebook (open sourced) to fine-tune llama-3-8b on a specific knowledge generated by the huge Llama-3-405B model š https://t.co/3nNScNkuJj
FireworksAI has launched fine-tuning services for its Llama 3.1 models, specifically the 8B and 70B versions, enhancing their usability for domain-specific applications. Users can now leverage these models to create specialized versions by utilizing the capabilities of larger models, such as the Llama 3.0 405B. The integration with Weights & Biases allows for easy monitoring of the fine-tuning process. Additionally, resources and guides have been made available, including a comprehensive guide on supervised fine-tuning on Hugging Face and a new guide focusing on LLM optimization techniques, particularly quantization. These developments are expected to significantly improve the efficiency and effectiveness of model training in various applications.