tensorblock/zwhe99_Qwen2.5-3B-orz-GGUF

zwhe99/Qwen2.5-3B-orz - GGUF

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This repo contains GGUF format model files for zwhe99/Qwen2.5-3B-orz.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

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Prompt template

A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: You must put your answer inside <answer> </answer> tags, i.e., <answer> answer here </answer>. And your final answer will be extracted automatically by the \boxed{} tag.
This is the problem:
{prompt}
Assistant: <think>

Model file specification

Filename	Quant type	File Size	Description
Qwen2.5-3B-orz-Q2_K.gguf	Q2_K	1.275 GB	smallest, significant quality loss - not recommended for most purposes
Qwen2.5-3B-orz-Q3_K_S.gguf	Q3_K_S	1.454 GB	very small, high quality loss
Qwen2.5-3B-orz-Q3_K_M.gguf	Q3_K_M	1.590 GB	very small, high quality loss
Qwen2.5-3B-orz-Q3_K_L.gguf	Q3_K_L	1.707 GB	small, substantial quality loss
Qwen2.5-3B-orz-Q4_0.gguf	Q4_0	1.823 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Qwen2.5-3B-orz-Q4_K_S.gguf	Q4_K_S	1.834 GB	small, greater quality loss
Qwen2.5-3B-orz-Q4_K_M.gguf	Q4_K_M	1.930 GB	medium, balanced quality - recommended
Qwen2.5-3B-orz-Q5_0.gguf	Q5_0	2.170 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Qwen2.5-3B-orz-Q5_K_S.gguf	Q5_K_S	2.170 GB	large, low quality loss - recommended
Qwen2.5-3B-orz-Q5_K_M.gguf	Q5_K_M	2.225 GB	large, very low quality loss - recommended
Qwen2.5-3B-orz-Q6_K.gguf	Q6_K	2.538 GB	very large, extremely low quality loss
Qwen2.5-3B-orz-Q8_0.gguf	Q8_0	3.285 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/zwhe99_Qwen2.5-3B-orz-GGUF --include "Qwen2.5-3B-orz-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/zwhe99_Qwen2.5-3B-orz-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'