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

Libraries

How to use rzzhan/ExGRPO-Qwen2.5-7B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rzzhan/ExGRPO-Qwen2.5-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("rzzhan/ExGRPO-Qwen2.5-7B-Instruct", device_map="auto")
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 Settings

vLLM

How to use rzzhan/ExGRPO-Qwen2.5-7B-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rzzhan/ExGRPO-Qwen2.5-7B-Instruct

SGLang

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

Docker Model Runner
How to use rzzhan/ExGRPO-Qwen2.5-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/rzzhan/ExGRPO-Qwen2.5-7B-Instruct
```

Add model card for ExGRPO: Learning to Reason from Experience

by nielsr HF Staff - opened Oct 4, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+63

-0

nielsr

Oct 4, 2025

This PR adds a comprehensive model card for the ExGRPO model.
It includes:

Relevant metadata (license: apache-2.0, library_name: transformers, pipeline_tag: text-generation).
A link to the paper: ExGRPO: Learning to Reason from Experience.
A link to the official GitHub repository: https://github.com/ElliottYan/LUFFY/tree/main/ExGRPO.
An overview of the model, including key highlights and an illustrative image from the GitHub README.
A link to the Hugging Face Collection associated with ExGRPO.
The BibTeX citation for the paper.

These additions will significantly improve the discoverability and usability of this model on the Hugging Face Hub. Please review and merge.

Add model card for ExGRPO: Learning to Reason from Experience82217fca

rzzhan changed pull request status to merged Oct 24, 2025

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