Instructions to use bmbgsj/ProRAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bmbgsj/ProRAG with Transformers:

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

pipe = pipeline("text-generation", model="bmbgsj/ProRAG")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bmbgsj/ProRAG")
model = AutoModelForCausalLM.from_pretrained("bmbgsj/ProRAG")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bmbgsj/ProRAG with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bmbgsj/ProRAG

SGLang

How to use bmbgsj/ProRAG 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 "bmbgsj/ProRAG" \
    --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": "bmbgsj/ProRAG",
		"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 "bmbgsj/ProRAG" \
        --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": "bmbgsj/ProRAG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bmbgsj/ProRAG with Docker Model Runner:
```
docker model run hf.co/bmbgsj/ProRAG
```

bmbgsj commited on Feb 4

Commit

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+---
+language:
+- en
+- zh
+license: apache-2.0
+library_name: transformers
+tags:
+- qwen3
+- text-generation
+- casual-lm
+base_model: Qwen/Qwen3-8B
+pipeline_tag: text-generation
+arxiv: 2601.21912
+---
+# Model Card for ProRAG
+This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) based on the methodology described in the paper associated with arXiv ID: **2601.21912**.
+## Model Details
+- **Base Model:** Qwen3-8B
+- **Language:** English, Chinese (and others supported by Qwen3)
+- **Paper:** [View on arXiv](https://arxiv.org/abs/2601.21912)
+- **Library:** Transformers
+## 💻 Code & Inference
+For inference code, usage examples, and reproduction scripts, please refer to our GitHub repository:
+👉 **[Click here to view the GitHub Repository](https://github.com/lilinwz/ProRAG/tree/main)**
+*(Please verify the details and instructions on the GitHub page.)*
+## Citation
+If you use this model or the associated paper in your research, please cite:
+```bibtex
+@misc{wang2026proragprocesssupervisedreinforcementlearning,
+      title={ProRAG: Process-Supervised Reinforcement Learning for Retrieval-Augmented Generation},
+      author={Zhao Wang and Ziliang Zhao and Zhicheng Dou},
+      year={2026},
+      eprint={2601.21912},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2601.21912},
+}
+```