Transformers
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Inference Endpoints

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This toolbox already includes 18 Jupyter notebooks specially optimized for Llama 3.1, Llama 3.2, and Llama 3.3 LLMs. The logs of successful runs are also provided. More notebooks will be regularly added.

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To run the code in the toolbox, CUDA 12.4 and PyTorch 2.4 are recommended. PyTorch 2.5 might already work but I didn't test it yet.

Toolbox content

  • Supervised Fine-Tuning with Chat Templates (6 notebooks)

    • Full fine-tuning

    • LoRA fine-tuning

    • LoRA fine-tuning (with Llama 3.1/3.2 Instruct)

    • Multi-GPU QLoRA/LoRA fine-tuning with FSDP (with Llama 3.1/3.2/3.3 Instruct)

    • QLoRA fine-tuning with Bitsandbytes quantization

    • QLoRA fine-tuning with AutoRound quantization

    • LoRA and QLoRA fine-tuning with Unsloth

  • Preference Optimization (2 notebooks)

    • DPO training with LoRA (TRL and Transformers)

    • ORPO training with LoRA (TRL and Transformers)

    • Multi-GPU QLoRA/LoRA DPO Training with FSDP

  • Quantization (3 notebooks)

    • AWQ

    • AutoRound

    • GGUF for llama.cpp

  • Inference (4 notebooks)

    • Transformers with and without a LoRA adapter

    • vLLM offline and online inference

    • Ollama (not released yet)

    • llama.cpp

  • Merging (3 notebooks)

    • Merge a LoRA adapter into the base model

    • Merge a QLoRA adapter into the base model

    • Merge several Llama 3.1/3.2 models into one with mergekit (not released yet)

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Datasets used to train kaitchup-toolboxes/Llama3