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Reminder to use the dev version Transformers:

pip install git+https://github.com/huggingface/transformers.git

Finetune Gemma 2, Llama 3.1, Mistral 2-5x faster with 70% less memory via Unsloth!

Directly quantized 4bit model with bitsandbytes.

We have a Google Colab Tesla T4 notebook for Gemma 2 (2B) here: https://colab.research.google.com/drive/1weTpKOjBZxZJ5PQ-Ql8i6ptAY2x-FWVA?usp=sharing

We have a Google Colab Tesla T4 notebook for Gemma 2 (9B) here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports Free Notebooks Performance Memory use
Llama 3 (8B) ▶️ Start on Colab 2.4x faster 58% less
Gemma 2 (9B) ▶️ Start on Colab 2x faster 63% less
Mistral (9B) ▶️ Start on Colab 2.2x faster 62% less
Phi 3 (mini) ▶️ Start on Colab 2x faster 63% less
TinyLlama ▶️ Start on Colab 3.9x faster 74% less
CodeLlama (34B) A100 ▶️ Start on Colab 1.9x faster 27% less
Mistral (7B) 1xT4 ▶️ Start on Kaggle 5x faster* 62% less
DPO - Zephyr ▶️ Start on Colab 1.9x faster 19% less
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