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tensorblock/RoadQAQ_Qwen2.5-Math-1.5B-16k-think-GGUF

sourceHugging Facemitupdated 8mo agoView on Hugging Face
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RoadQAQ/Qwen2.5-Math-1.5B-16k-think - GGUF

<div style="text-align: left; margin: 20px 0;"> <a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Join our Discord to learn more about what we're building โ†— </a> </div>

This repo contains GGUF format model files for RoadQAQ/Qwen2.5-Math-1.5B-16k-think.

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

Your task is to follow a systematic, thorough reasoning process before providing the final solution. This involves analyzing, summarizing, exploring, reassessing, and refining your thought process through multiple iterations. Structure your response into two sections: Thought and Solution. In the Thought section, present your reasoning using the format: โ€œ<think>
 {thoughts} </think>
โ€. Each thought should include detailed analysis, brainstorming, verification, and refinement of ideas. After โ€œ</think>
,โ€ in the Solution section, provide the final, logical, and accurate answer, clearly derived from the exploration in the Thought section. If applicable, include the answer in oxed{} for closed-form results like multiple choices or mathematical solutions. User: This is the problem:
{prompt}
Assistant: <think>

Model file specification

FilenameQuant typeFile SizeDescription
Qwen2.5-Math-1.5B-16k-think-Q2_K.ggufQ2_K0.676 GBsmallest, significant quality loss - not recommended for most purposes
Qwen2.5-Math-1.5B-16k-think-Q3_K_S.ggufQ3KS0.761 GBvery small, high quality loss
Qwen2.5-Math-1.5B-16k-think-Q3_K_M.ggufQ3KM0.824 GBvery small, high quality loss
Qwen2.5-Math-1.5B-16k-think-Q3_K_L.ggufQ3KL0.880 GBsmall, substantial quality loss
Qwen2.5-Math-1.5B-16k-think-Q4_0.ggufQ4_00.935 GBlegacy; small, very high quality loss - prefer using Q3KM
Qwen2.5-Math-1.5B-16k-think-Q4_K_S.ggufQ4KS0.940 GBsmall, greater quality loss
Qwen2.5-Math-1.5B-16k-think-Q4_K_M.ggufQ4KM0.986 GBmedium, balanced quality - recommended
Qwen2.5-Math-1.5B-16k-think-Q5_0.ggufQ5_01.099 GBlegacy; medium, balanced quality - prefer using Q4KM
Qwen2.5-Math-1.5B-16k-think-Q5_K_S.ggufQ5KS1.099 GBlarge, low quality loss - recommended
Qwen2.5-Math-1.5B-16k-think-Q5_K_M.ggufQ5KM1.125 GBlarge, very low quality loss - recommended
Qwen2.5-Math-1.5B-16k-think-Q6_K.ggufQ6_K1.273 GBvery large, extremely low quality loss
Qwen2.5-Math-1.5B-16k-think-Q8_0.ggufQ8_01.647 GBvery large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

shell
pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

shell
huggingface-cli download tensorblock/RoadQAQ_Qwen2.5-Math-1.5B-16k-think-GGUF --include "Qwen2.5-Math-1.5B-16k-think-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:

shell
huggingface-cli download tensorblock/RoadQAQ_Qwen2.5-Math-1.5B-16k-think-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'