Quantization made by Richard Erkhov.
gemma-2-2b - GGUF
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/gemma-2-2b/
| Name | Quant method | Size |
|---|---|---|
| gemma-2-2b.Q2_K.gguf | Q2_K | 1.15GB |
| gemma-2-2b.IQ3_XS.gguf | IQ3_XS | 1.22GB |
| gemma-2-2b.IQ3_S.gguf | IQ3_S | 1.27GB |
| gemma-2-2b.Q3_K_S.gguf | Q3_K_S | 1.27GB |
| gemma-2-2b.IQ3_M.gguf | IQ3_M | 1.3GB |
| gemma-2-2b.Q3_K.gguf | Q3_K | 1.36GB |
| gemma-2-2b.Q3_K_M.gguf | Q3_K_M | 1.36GB |
| gemma-2-2b.Q3_K_L.gguf | Q3_K_L | 1.44GB |
| gemma-2-2b.IQ4_XS.gguf | IQ4_XS | 1.47GB |
| gemma-2-2b.Q4_0.gguf | Q4_0 | 1.52GB |
| gemma-2-2b.IQ4_NL.gguf | IQ4_NL | 1.53GB |
| gemma-2-2b.Q4_K_S.gguf | Q4_K_S | 1.53GB |
| gemma-2-2b.Q4_K.gguf | Q4_K | 1.59GB |
| gemma-2-2b.Q4_K_M.gguf | Q4_K_M | 1.59GB |
| gemma-2-2b.Q4_1.gguf | Q4_1 | 1.64GB |
| gemma-2-2b.Q5_0.gguf | Q5_0 | 1.75GB |
| gemma-2-2b.Q5_K_S.gguf | Q5_K_S | 1.75GB |
| gemma-2-2b.Q5_K.gguf | Q5_K | 1.79GB |
| gemma-2-2b.Q5_K_M.gguf | Q5_K_M | 1.79GB |
| gemma-2-2b.Q5_1.gguf | Q5_1 | 1.87GB |
| gemma-2-2b.Q6_K.gguf | Q6_K | 2.0GB |
| gemma-2-2b.Q8_0.gguf | Q8_0 | 2.59GB |
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
✨ 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.