You can deploy this model and start earning money today!

tensorblock/
gemma-2-27b-it-SimPO-37K-GGUF

Name: gemma-2-27b-it-SimPO-37K-GGUF
Availability: InStock

Views

No views yet

AALF/gemma-2-27b-it-SimPO-37K - GGUF

This repo contains GGUF format model files for AALF/gemma-2-27b-it-SimPO-37K.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Forge
Forge Project
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers	TensorBlock Studio
MCP Servers	Studio
A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀	👀 See what we built 👀

## Prompt template

<bos><start_of_turn>user
{system_prompt}

{prompt}<end_of_turn>
<start_of_turn>model

Filename	Quant type	File Size	Description
gemma-2-27b-it-SimPO-37K-Q2_K.gguf	Q2_K	9.732 GB	smallest, significant quality loss - not recommended for most purposes
gemma-2-27b-it-SimPO-37K-Q3_K_S.gguf	Q3_K_S	11.333 GB	very small, high quality loss
gemma-2-27b-it-SimPO-37K-Q3_K_M.gguf	Q3_K_M	12.503 GB	very small, high quality loss
gemma-2-27b-it-SimPO-37K-Q3_K_L.gguf	Q3_K_L	13.522 GB	small, substantial quality loss
gemma-2-27b-it-SimPO-37K-Q4_0.gguf	Q4_0	14.555 GB	legacy; small, very high quality loss - prefer using Q3_K_M
gemma-2-27b-it-SimPO-37K-Q4_K_S.gguf	Q4_K_S	14.658 GB	small, greater quality loss
gemma-2-27b-it-SimPO-37K-Q4_K_M.gguf	Q4_K_M	15.502 GB	medium, balanced quality - recommended
gemma-2-27b-it-SimPO-37K-Q5_0.gguf	Q5_0	17.587 GB	legacy; medium, balanced quality - prefer using Q4_K_M
gemma-2-27b-it-SimPO-37K-Q5_K_S.gguf	Q5_K_S	17.587 GB	large, low quality loss - recommended
gemma-2-27b-it-SimPO-37K-Q5_K_M.gguf	Q5_K_M	18.075 GB	large, very low quality loss - recommended
gemma-2-27b-it-SimPO-37K-Q6_K.gguf	Q6_K	20.809 GB	very large, extremely low quality loss
gemma-2-27b-it-SimPO-37K-Q8_0.gguf	Q8_0	26.950 GB	very large, extremely low quality loss - not recommended

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-2-27b-it-SimPO-37K-GGUF --include "gemma-2-27b-it-SimPO-37K-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-2-27b-it-SimPO-37K-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'