QwQ-Math-IO-500M-GGUF Open-source Mathematical Reasoning Model

Qwq Math IO 500M GGUF

Developed by tensorblock

QwQ-Math-IO-500M is a 500M-parameter language model focused on mathematical reasoning and input-output processing, offering quantized versions in GGUF format.

Large Language Model EnglishOpen Source License:Apache-2.0 #Mathematical Reasoning #Lightweight Model #GGUF Quantization

Downloads 56

Release Time : 3/7/2025

Model Overview

This model specializes in mathematical reasoning tasks, supporting text generation for scenarios requiring mathematical problem-solving capabilities.

Model Features

Mathematical Reasoning Capability

Specializes in understanding and solving mathematical problems, suitable for scenarios requiring mathematical reasoning.

Multiple Quantized Versions

Offers various quantized versions from Q2_K to Q8_0 to meet different hardware and performance needs.

Lightweight

With 500M parameters, it maintains performance while having lower hardware requirements.

Model Capabilities

Mathematical Problem Solving

Text Generation

Input-Output Processing

Use Cases

Education

Math Problem Solving

Helps students understand and solve mathematical problems

Provides detailed solution steps and answers

Research

Mathematical Reasoning Research

Used for research on mathematical reasoning and logical problems

Provides benchmark tests for mathematical reasoning

🚀 prithivMLmods/QwQ-Math-IO-500M - GGUF

This repository provides GGUF format model files for prithivMLmods/QwQ-Math-IO-500M. These files are quantized using machines from TensorBlock, and are compatible with llama.cpp as of commit b4823.

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

✨ Features

Our projects

Project	Description	Image	Link
Awesome MCP Servers	A comprehensive collection of Model Context Protocol (MCP) servers.		👉 See what we built 👉
TensorBlock Studio	A lightweight, open, and extensible multi-LLM interaction studio.		👉 See what we built 👉

📚 Documentation

Prompt template

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

Model file specification

Property	Details
Model Type	GGUF format model files for prithivMLmods/QwQ-Math-IO-500M
Filename and Quantization
QwQ-Math-IO-500M-Q2_K.gguf	Q2_K, 0.339 GB, smallest, significant quality loss - not recommended for most purposes
QwQ-Math-IO-500M-Q3_K_S.gguf	Q3_K_S, 0.338 GB, very small, high quality loss
QwQ-Math-IO-500M-Q3_K_M.gguf	Q3_K_M, 0.355 GB, very small, high quality loss
QwQ-Math-IO-500M-Q3_K_L.gguf	Q3_K_L, 0.369 GB, small, substantial quality loss
QwQ-Math-IO-500M-Q4_0.gguf	Q4_0, 0.352 GB, legacy; small, very high quality loss - prefer using Q3_K_M
QwQ-Math-IO-500M-Q4_K_S.gguf	Q4_K_S, 0.385 GB, small, greater quality loss
QwQ-Math-IO-500M-Q4_K_M.gguf	Q4_K_M, 0.398 GB, medium, balanced quality - recommended
QwQ-Math-IO-500M-Q5_0.gguf	Q5_0, 0.397 GB, legacy; medium, balanced quality - prefer using Q4_K_M
QwQ-Math-IO-500M-Q5_K_S.gguf	Q5_K_S, 0.413 GB, large, low quality loss - recommended
QwQ-Math-IO-500M-Q5_K_M.gguf	Q5_K_M, 0.420 GB, large, very low quality loss - recommended
QwQ-Math-IO-500M-Q6_K.gguf	Q6_K, 0.506 GB, very large, extremely low quality loss
QwQ-Math-IO-500M-Q8_0.gguf	Q8_0, 0.531 GB, very large, extremely low quality loss - not recommended

📦 Installation

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, download the individual model file to a local directory

huggingface-cli download tensorblock/QwQ-Math-IO-500M-GGUF --include "QwQ-Math-IO-500M-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you want to download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/QwQ-Math-IO-500M-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

📄 License

This project is licensed under the Apache-2.0 license.

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