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

Libraries

How to use mukel/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="mukel/Qwen2.5-Math-7B-Instruct-GGUF",
	filename="Qwen2.5-Math-7B-Instruct-Q4_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mukel/Qwen2.5-Math-7B-Instruct-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 mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

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 mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

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 mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

Use Docker

docker model run hf.co/mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use mukel/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 "mukel/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": "mukel/Qwen2.5-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

Ollama
How to use mukel/Qwen2.5-Math-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0
```

Unsloth Studio

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

How to use mukel/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 serve -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

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": "mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mukel/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 serve -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

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 mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mukel/Qwen2.5-Math-7B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use mukel/Qwen2.5-Math-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mukel/Qwen2.5-Math-7B-Instruct-GGUF:Q4_0

Run and chat with the model

lemonade run user.Qwen2.5-Math-7B-Instruct-GGUF-Q4_0

List all available models

lemonade list

mukel commited on Sep 27, 2024

Commit

2221c99

verified ·

1 Parent(s): b00c8c0

Update README.md

Browse files

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README.md +42 -3

README.md CHANGED Viewed

@@ -1,3 +1,42 @@
----
-license: apache-2.0
----

+---
+base_model: Qwen/Qwen2.5-Math-7B-Instruct
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+quantized_by: mukel
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE
+---
+> [!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>
+# GGUF models for qwen2.java
+Pure .gguf Q4_0 and Q8_0 quantizations of Qwen 2.5 models, ready to consume by `qwen2.java`.
+In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the token embeddings are quantized with Q6_K, instead of Q4_0.
+A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the llama-quantize utility from llama.cpp as follows:
+```
+./llama-quantize --pure ./Qwen-2.5-7B-Instruct-BF16.gguf ./Qwen-2.5-7B-Instruct-Q4_0.gguf Q4_0
+```
+## 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).