Instructions to use GavinChan1105/X-R1-3B-cn-math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GavinChan1105/X-R1-3B-cn-math with Transformers:

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

pipe = pipeline("text-generation", model="GavinChan1105/X-R1-3B-cn-math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GavinChan1105/X-R1-3B-cn-math")
model = AutoModelForCausalLM.from_pretrained("GavinChan1105/X-R1-3B-cn-math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GavinChan1105/X-R1-3B-cn-math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GavinChan1105/X-R1-3B-cn-math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GavinChan1105/X-R1-3B-cn-math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GavinChan1105/X-R1-3B-cn-math

SGLang

How to use GavinChan1105/X-R1-3B-cn-math 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 "GavinChan1105/X-R1-3B-cn-math" \
    --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": "GavinChan1105/X-R1-3B-cn-math",
		"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 "GavinChan1105/X-R1-3B-cn-math" \
        --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": "GavinChan1105/X-R1-3B-cn-math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GavinChan1105/X-R1-3B-cn-math with Docker Model Runner:
```
docker model run hf.co/GavinChan1105/X-R1-3B-cn-math
```

X-R1-3B-cn-math / README.md

GavinChan1105

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	---
	base_model: Qwen/Qwen2.5-3B
	datasets: xiaodongguaAIGC/X-R1-TAL-SCQ5K
	library_name: transformers
	tags:
	- generated_from_trainer
	- X-R1
	licence: license
	---

	# Model Card for None

	This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) on the [xiaodongguaAIGC/X-R1-TAL-SCQ5K](https://huggingface.co/datasets/xiaodongguaAIGC/X-R1-TAL-SCQ5K) dataset.
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	from transformers import pipeline

	question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
	generator = pipeline("text-generation", model="None", device="cuda")
	output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
	print(output["generated_text"])
	```

	## Training procedure

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ccseu1991-southeast-university/ma-rlhf/runs/ry4wf65o)


	This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).

	### Framework versions

	- TRL: 0.16.0.dev0
	- Transformers: 4.48.2
	- Pytorch: 2.5.1
	- Datasets: 3.2.0
	- Tokenizers: 0.21.4

	## Citations

	Cite GRPO as:

	```bibtex
	@article{zhihong2024deepseekmath,
	title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
	author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
	year = 2024,
	eprint = {arXiv:2402.03300},
	}

	```

	Cite TRL as:

	```bibtex
	@misc{vonwerra2022trl,
	title = {{TRL: Transformer Reinforcement Learning}},
	author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```