Instructions to use QuantFactory/Qwen2-Math-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Qwen2-Math-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Qwen2-Math-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Qwen2-Math-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Qwen2-Math-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Qwen2-Math-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Qwen2-Math-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Qwen2-Math-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Qwen2-Math-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Qwen2-Math-7B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Qwen2-Math-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2-Math-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2-Math-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-Math-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Qwen2-Math-7B-GGUF / README.md

munish0838

Upload README.md with huggingface_hub

f188050 verified about 2 years ago

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	---

	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- chat

	---

	![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)

	# QuantFactory/Qwen2-Math-7B-GGUF
	This is quantized version of [Qwen/Qwen2-Math-7B](https://huggingface.co/Qwen/Qwen2-Math-7B) created using llama.cpp

	# Original Model Card



	# Qwen2-Math-7B

	> [!Warning]
	> <div align="center">
	> <b>
	> 🚨 Temporarily this model mainly supports English. We will release bilingual (English & Chinese) models soon!
	> </b>
	> </div>

	## Introduction

	Over the past year, we have dedicated significant effort to researching and enhancing the reasoning capabilities of large language models, with a particular focus on their ability to solve arithmetic and mathematical problems. Today, we are delighted to introduce a serise of math-specific large language models of our Qwen2 series, Qwen2-Math and Qwen2-Math-Instruct-1.5B/7B/72B. Qwen2-Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT4o). We hope that Qwen2-Math can contribute to the scientific community for solving advanced mathematical problems that require complex, multi-step logical reasoning.


	## Model Details


	For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2-Math).


	## Requirements
	* `transformers>=4.40.0` for Qwen2-Math models. The latest version is recommended.

	> [!Warning]
	> <div align="center">
	> <b>
	> 🚨 This is a must because `transformers` integrated Qwen2 codes since `4.37.0`.
	> </b>
	> </div>

	For requirements on GPU memory and the respective throughput, see similar results of Qwen2 [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).

	> [!Important]
	>
	> Qwen2-Math-7B-Instruct is an instruction model for chatting;
	>
	> Qwen2-Math-7B is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
	>

	## Citation

	If you find our work helpful, feel free to give us a citation.

	```
	@article{yang2024qwen2,
	title={Qwen2 technical report},
	author={Yang, An and Yang, Baosong and Hui, Binyuan and Zheng, Bo and Yu, Bowen and Zhou, Chang and Li, Chengpeng and Li, Chengyuan and Liu, Dayiheng and Huang, Fei and others},
	journal={arXiv preprint arXiv:2407.10671},
	year={2024}
	}
	```