Instructions to use Jarvis1111/DoctorAgent-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jarvis1111/DoctorAgent-RL with Transformers:

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

pipe = pipeline("text-generation", model="Jarvis1111/DoctorAgent-RL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jarvis1111/DoctorAgent-RL")
model = AutoModelForCausalLM.from_pretrained("Jarvis1111/DoctorAgent-RL")
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 Jarvis1111/DoctorAgent-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jarvis1111/DoctorAgent-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jarvis1111/DoctorAgent-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jarvis1111/DoctorAgent-RL

SGLang

How to use Jarvis1111/DoctorAgent-RL 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 "Jarvis1111/DoctorAgent-RL" \
    --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": "Jarvis1111/DoctorAgent-RL",
		"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 "Jarvis1111/DoctorAgent-RL" \
        --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": "Jarvis1111/DoctorAgent-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jarvis1111/DoctorAgent-RL with Docker Model Runner:
```
docker model run hf.co/Jarvis1111/DoctorAgent-RL
```

Add comprehensive model card

by nielsr HF Staff - opened Jul 28, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+81

-3

Add comprehensive model card06f76b6a

nielsr

Jul 28, 2025

This PR adds a comprehensive model card for the DoctorAgent-RL model.

It includes:

Relevant metadata: pipeline_tag: text-generation and library_name: transformers.
A detailed description of the model and its key features.
Links to the research paper (DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue) and the GitHub repository (https://github.com/JarvisUSTC/DoctorAgent-RL).
An example of how to use the model with the transformers library for multi-turn clinical dialogue.
Citation information.

This will make the model more discoverable and easier to use for the community.

Jarvis1111 changed pull request status to merged Jul 29, 2025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment