metadata
license: gemma
library_name: transformers
base_model: nbeerbower/gemma2-gutenberg-27B
datasets:
- jondurbin/gutenberg-dpo-v0.1
tags:
- TensorBlock
- GGUF
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
nbeerbower/gemma2-gutenberg-27B - GGUF
This repo contains GGUF format model files for nbeerbower/gemma2-gutenberg-27B.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
Prompt template
<bos><start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| gemma2-gutenberg-27B-Q2_K.gguf | Q2_K | 9.732 GB | smallest, significant quality loss - not recommended for most purposes |
| gemma2-gutenberg-27B-Q3_K_S.gguf | Q3_K_S | 11.333 GB | very small, high quality loss |
| gemma2-gutenberg-27B-Q3_K_M.gguf | Q3_K_M | 12.503 GB | very small, high quality loss |
| gemma2-gutenberg-27B-Q3_K_L.gguf | Q3_K_L | 13.522 GB | small, substantial quality loss |
| gemma2-gutenberg-27B-Q4_0.gguf | Q4_0 | 14.555 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| gemma2-gutenberg-27B-Q4_K_S.gguf | Q4_K_S | 14.658 GB | small, greater quality loss |
| gemma2-gutenberg-27B-Q4_K_M.gguf | Q4_K_M | 15.502 GB | medium, balanced quality - recommended |
| gemma2-gutenberg-27B-Q5_0.gguf | Q5_0 | 17.587 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| gemma2-gutenberg-27B-Q5_K_S.gguf | Q5_K_S | 17.587 GB | large, low quality loss - recommended |
| gemma2-gutenberg-27B-Q5_K_M.gguf | Q5_K_M | 18.075 GB | large, very low quality loss - recommended |
| gemma2-gutenberg-27B-Q6_K.gguf | Q6_K | 20.809 GB | very large, extremely low quality loss |
| gemma2-gutenberg-27B-Q8_0.gguf | Q8_0 | 26.950 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/gemma2-gutenberg-27B-GGUF --include "gemma2-gutenberg-27B-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/gemma2-gutenberg-27B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'