metadata
language:
- en
library_name: transformers
license: gemma
tags:
- unsloth
- transformers
- gemma2
- gemma
- TensorBlock
- GGUF
base_model: unsloth/gemma-2-9b
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
unsloth/gemma-2-9b - GGUF
This repo contains GGUF format model files for unsloth/gemma-2-9b.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| gemma-2-9b-Q2_K.gguf | Q2_K | 3.544 GB | smallest, significant quality loss - not recommended for most purposes |
| gemma-2-9b-Q3_K_S.gguf | Q3_K_S | 4.040 GB | very small, high quality loss |
| gemma-2-9b-Q3_K_M.gguf | Q3_K_M | 4.435 GB | very small, high quality loss |
| gemma-2-9b-Q3_K_L.gguf | Q3_K_L | 4.780 GB | small, substantial quality loss |
| gemma-2-9b-Q4_0.gguf | Q4_0 | 5.069 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| gemma-2-9b-Q4_K_S.gguf | Q4_K_S | 5.103 GB | small, greater quality loss |
| gemma-2-9b-Q4_K_M.gguf | Q4_K_M | 5.365 GB | medium, balanced quality - recommended |
| gemma-2-9b-Q5_0.gguf | Q5_0 | 6.038 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| gemma-2-9b-Q5_K_S.gguf | Q5_K_S | 6.038 GB | large, low quality loss - recommended |
| gemma-2-9b-Q5_K_M.gguf | Q5_K_M | 6.191 GB | large, very low quality loss - recommended |
| gemma-2-9b-Q6_K.gguf | Q6_K | 7.068 GB | very large, extremely low quality loss |
| gemma-2-9b-Q8_0.gguf | Q8_0 | 9.152 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
huggingface-cli download tensorblock/gemma-2-9b-GGUF --include "gemma-2-9b-Q2_K.gguf" --local-dir MY_LOCAL_DIR
If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:
huggingface-cli download tensorblock/gemma-2-9b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'