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unsloth_-_gemma-2-2b-4bits
RichardErkhov/unsloth_-_gemma-2-2b-4bits
unsloth_-_gemma-2-2b-4bits is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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2.6B
2.3 GB on disk
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.safetensors2.3 GB · 99%
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How the weights are stored.
U82B · 77%
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
gemma-2-2b - bnb 4bits
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/gemma-2-2b/
Original model description:
language:
- en library_name: transformers license: gemma tags:
- unsloth
- transformers
- gemma2
- gemma
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
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/> <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/> <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
✨ 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 |
- This conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
- This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
- * Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
- Type
- gemma2
- Created
- Oct 11, 2024
- Updated
- Oct 11, 2024