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How to fine-tune open LLMs in 2025 with Hugging Face

philschmid.de · published 20 December 2024

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How to Fine-Tune LLMs in 2024 with Hugging Face Read at the source ↗

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https://github.com/Dao-AILab/flash-attention

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https://github.com/linkedin/Liger-Kernel

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https://huggingface.co/papers/2305.14314

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https://huggingface.co/papers/2406.06623

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https://huggingface.co/models

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https://distilabel.argilla.io/

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https://huggingface.co/datasets

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https://github.com/mlabonne/llm-datasets

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https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k

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https://huggingface.co/docs/trl/sft_trainer

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https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/training/scripts/run_sft.py

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https://github.com/axolotl-ai-cloud/axolotl

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https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/training/receipes/llama-3-1-8b-qlora.yaml

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https://huggingface.co/datasets/openai/gsm8k

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Evaluate LLMs and RAG a practical example using Langchain and Hugging Face Read at the source ↗

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Evaluate LLMs using Evaluation Harness and Hugging Face TGI/vLLM Read at the source ↗

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LLM Evaluation doesn't need to be complicated Read at the source ↗

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Evaluating Open LLMs with MixEval: The Closest Benchmark to LMSYS Chatbot Arena Read at the source ↗

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https://github.com/EleutherAI/lm-evaluation-harness

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https://github.com/huggingface/text-generation-inference

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https://github.com/cognitivecomputations/spectrum/tree/main/model_snr_results

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https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/training/receipes/llama-3-1-8b-spectrum.yaml

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