Downloads · 30 days
0
unsloth_-_gemma-2-2b-exl2
RichardErkhov/unsloth_-_gemma-2-2b-exl2
unsloth_-_gemma-2-2b-exl2 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.
Access
Public
Updated Jan 18, 2025
Repo size
—
Likes
0
Public
Repo makeup
Click a slice to open those files.
.json1.5 MB · 100%
At a glance
- Access
- Public
- Created
- Jan 18, 2025
- Updated
- Jan 18, 2025
- SHA
- c5c3a29d
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
gemma-2-2b - EXL2
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/gemma-2-2b/
Available sizes
| Branch | Bits | Description |
|---|---|---|
| 8_0 | 8.0 | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| 6_5 | 6.5 | Very similar to 8.0, good tradeoff of size vs performance, recommended. |
| 5_0 | 5.0 | Slightly lower quality vs 6.5, but usable |
| 4_25 | 4.25 | GPTQ equivalent bits per weight, slightly higher quality. |
| 3_5 | 3.5 | Lower quality, only use if you have to. |
Download instructions
With git:
git clone --single-branch --branch 6_5 https://huggingface.co/unsloth_-_gemma-2-2b-exl2 gemma-2-2b-6_5
With huggingface hub:
pip3 install huggingface-hub
To download a specific branch, use the --revision parameter. For example, to download the 6.5 bpw branch:
Linux:
huggingface-cli download unsloth_-_gemma-2-2b-exl2 --revision 6_5 --local-dir gemma-2-2b-6_5 --local-dir-use-symlinks False
Windows (which apparently doesn't like _ in folders sometimes?):
huggingface-cli download unsloth_-_gemma-2-2b-exl2 --revision 6_5 --local-dir gemma-2-2b-6.5 --local-dir-use-symlinks False
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.
- Created
- Jan 18, 2025
- Updated
- Jan 18, 2025