---
language:
- en
library_name: transformers
extra_gated_prompt: >-
  To gain access, [subscribe to The Kaitchup
  Pro](https://newsletter.kaitchup.com/subscribe). You will receive an access
  token for all the toolboxes in your welcome email. You can also purchase an
  access specifically for this repository on
  [Gumroad](https://benjaminmarie.gumroad.com/l/qwen2-5-toolbox). Once you have access, you can request for help and suggest new notebooks through the community tab.
datasets:
- mlabonne/orpo-dpo-mix-40k
- HuggingFaceH4/ultrachat_200k
---

This toolbox already includes 16 Jupyter notebooks specially optimized for [Qwen2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e). The logs of successful runs are also provided. More notebooks will be regularly added.

Once you've subscribed to The Kaitchup Pro or purchased access, you can also request repository access here.

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 (5 notebooks)

  * Full fine-tuning 

  * LoRA fine-tuning

  * QLoRA fine-tuning with Bitsandbytes quantization 

  * QLoRA fine-tuning with AutoRound quantization 

  * LoRA and QLoRA fine-tuning with Unsloth 

* Preference Optimization (3 notebooks)

  * Full DPO training (TRL and Transformers)

  * DPO training with LoRA (TRL and Transformers)

  * ORPO training with LoRA (TRL and Transformers)

* Quantization (3 notebooks)

  * AWQ

  * AutoRound (with code to quantize Qwen 2.5 72B)

  * GGUF for llama.cpp

* Inference with Qwen2.5 Instruct and Your Own Fine-tuned Qwen2.5 (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 Qwen2.5 models into one with mergekit (not released yet)