Instructions to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF",
	filename="Qwen2.5-Math-7B-Instruct.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2.5-Math-7B-Instruct-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/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2.5-Math-7B-Instruct-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/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Qwen2.5-Math-7B-Instruct-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/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-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/Qwen2.5-Math-7B-Instruct-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/Qwen2.5-Math-7B-Instruct-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/Qwen2.5-Math-7B-Instruct-GGUF to start chatting

Pi new

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Math-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Qwen2.5-Math-7B-Instruct-GGUF / README.md

aashish1904

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	---

	base_model: Qwen/Qwen2.5-Math-7B
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- chat
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE

	---

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


	# QuantFactory/Qwen2.5-Math-7B-Instruct-GGUF
	This is quantized version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) created using llama.cpp

	# Original Model Card



	# Qwen2.5-Math-7B-Instruct

	> [!Warning]
	> <div align="center">
	> <b>
	> 🚨 Qwen2.5-Math mainly supports solving English and Chinese math problems through CoT and TIR. We do not recommend using this series of models for other tasks.
	> </b>
	> </div>

	## Introduction

	In August 2024, we released the first series of mathematical LLMs - [Qwen2-Math](https://qwenlm.github.io/blog/qwen2-math/) - of our Qwen family. A month later, we have upgraded it and open-sourced Qwen2.5-Math series, including base models Qwen2.5-Math-1.5B/7B/72B, instruction-tuned models Qwen2.5-Math-1.5B/7B/72B-Instruct, and mathematical reward model Qwen2.5-Math-RM-72B.

	Unlike Qwen2-Math series which only supports using Chain-of-Thught (CoT) to solve English math problems, Qwen2.5-Math series is expanded to support using both CoT and Tool-integrated Reasoning (TIR) to solve math problems in both Chinese and English. The Qwen2.5-Math series models have achieved significant performance improvements compared to the Qwen2-Math series models on the Chinese and English mathematics benchmarks with CoT.

	![](http://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2.5/qwen2.5-math-pipeline.jpeg)

	While CoT plays a vital role in enhancing the reasoning capabilities of LLMs, it faces challenges in achieving computational accuracy and handling complex mathematical or algorithmic reasoning tasks, such as finding the roots of a quadratic equation or computing the eigenvalues of a matrix. TIR can further improve the model's proficiency in precise computation, symbolic manipulation, and algorithmic manipulation. Qwen2.5-Math-1.5B/7B/72B-Instruct achieve 79.7, 85.3, and 87.8 respectively on the MATH benchmark using TIR.

	## Model Details


	For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2.5-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2.5-Math).


	## Requirements
	* `transformers>=4.37.0` for Qwen2.5-Math models. The latest version is recommended.

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

	## Quick Start

	> [!Important]
	>
	> Qwen2.5-Math-7B-Instruct is an instruction model for chatting;
	>
	> Qwen2.5-Math-7B is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
	>

	### 🤗 Hugging Face Transformers

	Qwen2.5-Math can be deployed and infered in the same way as [Qwen2.5](https://github.com/QwenLM/Qwen2.5). Here we show a code snippet to show you how to use the chat model with `transformers`:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "Qwen/Qwen2.5-Math-7B-Instruct"
	device = "cuda" # the device to load the model onto

	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained(model_name)

	prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."

	# CoT
	messages = [
	{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
	{"role": "user", "content": prompt}
	]

	# TIR
	messages = [
	{"role": "system", "content": "Please integrate natural language reasoning with programs to solve the problem above, and put your final answer within \\boxed{}."},
	{"role": "user", "content": prompt}
	]

	text = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True
	)
	model_inputs = tokenizer([text], return_tensors="pt").to(device)

	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=512
	)
	generated_ids = [
	output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
	]

	response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
	```

	## 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}
	}
	```