Orion-zhen/Qwen2.5-7B-Instruct-Uncensored - GGUF

This repo contains GGUF format model files for Orion-zhen/Qwen2.5-7B-Instruct-Uncensored.

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

Our projects

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

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename	Quant type	File Size	Description
Qwen2.5-7B-Instruct-Uncensored-Q2_K.gguf	Q2_K	3.016 GB	smallest, significant quality loss - not recommended for most purposes
Qwen2.5-7B-Instruct-Uncensored-Q3_K_S.gguf	Q3_K_S	3.492 GB	very small, high quality loss
Qwen2.5-7B-Instruct-Uncensored-Q3_K_M.gguf	Q3_K_M	3.808 GB	very small, high quality loss
Qwen2.5-7B-Instruct-Uncensored-Q3_K_L.gguf	Q3_K_L	4.088 GB	small, substantial quality loss
Qwen2.5-7B-Instruct-Uncensored-Q4_0.gguf	Q4_0	4.431 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Qwen2.5-7B-Instruct-Uncensored-Q4_K_S.gguf	Q4_K_S	4.458 GB	small, greater quality loss
Qwen2.5-7B-Instruct-Uncensored-Q4_K_M.gguf	Q4_K_M	4.683 GB	medium, balanced quality - recommended
Qwen2.5-7B-Instruct-Uncensored-Q5_0.gguf	Q5_0	5.315 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Qwen2.5-7B-Instruct-Uncensored-Q5_K_S.gguf	Q5_K_S	5.315 GB	large, low quality loss - recommended
Qwen2.5-7B-Instruct-Uncensored-Q5_K_M.gguf	Q5_K_M	5.445 GB	large, very low quality loss - recommended
Qwen2.5-7B-Instruct-Uncensored-Q6_K.gguf	Q6_K	6.254 GB	very large, extremely low quality loss
Qwen2.5-7B-Instruct-Uncensored-Q8_0.gguf	Q8_0	8.099 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/Qwen2.5-7B-Instruct-Uncensored-GGUF --include "Qwen2.5-7B-Instruct-Uncensored-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/Qwen2.5-7B-Instruct-Uncensored-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'