Instructions to use TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu with Transformers:

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

pipe = pipeline("text-generation", model="TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu

SGLang

How to use TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu 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 "TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu" \
    --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": "TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu",
		"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 "TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu" \
        --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": "TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu with Docker Model Runner:
```
docker model run hf.co/TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu
```

Model Description

Developed by: Qwen
Model type: Causal Language Models
License: Apache 2.0
Fine-tuned from: TOFU-SFT/Qwen2.5-Math-7B-4bit

Citation

@article{yang2024qwen25mathtechnicalreportmathematical,
  title={Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement}, 
  author={An Yang and Beichen Zhang and Binyuan Hui and Bofei Gao and Bowen Yu and Chengpeng Li and Dayiheng Liu and Jianhong Tu and Jingren Zhou and Junyang Lin and Keming Lu and Mingfeng Xue and Runji Lin and Tianyu Liu and Xingzhang Ren and Zhenru Zhang},
  journal={arXiv preprint arXiv:2409.12122},
  year={2024}
}

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Paper for TOFU-SFT/Qwen2.5-Math-7B-4bit-cot-sft-tofu

Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement

Paper • 2409.12122 • Published Sep 18, 2024 • 5