metadata
library_name: transformers
language:
- en
license: gemma
tags:
- gemma
- pytorch
- instruct
- finetune
- TensorBlock
- GGUF
base_model: lemon-mint/gemma-7b-openhermes-v0.80
pipeline_tag: text-generation
datasets:
- teknium/OpenHermes-2.5
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
lemon-mint/gemma-7b-openhermes-v0.80 - GGUF
This repo contains GGUF format model files for lemon-mint/gemma-7b-openhermes-v0.80.
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 |
|---|---|---|---|
| gemma-7b-openhermes-v0.80-Q2_K.gguf | Q2_K | 3.242 GB | smallest, significant quality loss - not recommended for most purposes |
| gemma-7b-openhermes-v0.80-Q3_K_S.gguf | Q3_K_S | 3.709 GB | very small, high quality loss |
| gemma-7b-openhermes-v0.80-Q3_K_M.gguf | Q3_K_M | 4.069 GB | very small, high quality loss |
| gemma-7b-openhermes-v0.80-Q3_K_L.gguf | Q3_K_L | 4.386 GB | small, substantial quality loss |
| gemma-7b-openhermes-v0.80-Q4_0.gguf | Q4_0 | 4.668 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| gemma-7b-openhermes-v0.80-Q4_K_S.gguf | Q4_K_S | 4.700 GB | small, greater quality loss |
| gemma-7b-openhermes-v0.80-Q4_K_M.gguf | Q4_K_M | 4.964 GB | medium, balanced quality - recommended |
| gemma-7b-openhermes-v0.80-Q5_0.gguf | Q5_0 | 5.570 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| gemma-7b-openhermes-v0.80-Q5_K_S.gguf | Q5_K_S | 5.570 GB | large, low quality loss - recommended |
| gemma-7b-openhermes-v0.80-Q5_K_M.gguf | Q5_K_M | 5.723 GB | large, very low quality loss - recommended |
| gemma-7b-openhermes-v0.80-Q6_K.gguf | Q6_K | 6.529 GB | very large, extremely low quality loss |
| gemma-7b-openhermes-v0.80-Q8_0.gguf | Q8_0 | 8.454 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-7b-openhermes-v0.80-GGUF --include "gemma-7b-openhermes-v0.80-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-7b-openhermes-v0.80-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'