Instructions to use mims-harvard/ATHENA-R1-Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mims-harvard/ATHENA-R1-Qwen3-8B with Transformers:

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

pipe = pipeline("text-generation", model="mims-harvard/ATHENA-R1-Qwen3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mims-harvard/ATHENA-R1-Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained("mims-harvard/ATHENA-R1-Qwen3-8B")
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 mims-harvard/ATHENA-R1-Qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mims-harvard/ATHENA-R1-Qwen3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mims-harvard/ATHENA-R1-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mims-harvard/ATHENA-R1-Qwen3-8B

SGLang

How to use mims-harvard/ATHENA-R1-Qwen3-8B 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 "mims-harvard/ATHENA-R1-Qwen3-8B" \
    --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": "mims-harvard/ATHENA-R1-Qwen3-8B",
		"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 "mims-harvard/ATHENA-R1-Qwen3-8B" \
        --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": "mims-harvard/ATHENA-R1-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mims-harvard/ATHENA-R1-Qwen3-8B with Docker Model Runner:
```
docker model run hf.co/mims-harvard/ATHENA-R1-Qwen3-8B
```

ATHENA-R1-Qwen3-8B

Project page: athena.openscientist.ai · Code: mims-harvard/ATHENA

ATHENA-R1 is an AI agent for treatment reasoning, trained through reinforcement learning over a universe of 212 biomedical tools. It performs multi-step reasoning — identifying what evidence is needed, selecting tools, and incorporating retrieved evidence into subsequent steps — with tool calls served through the ToolUniverse (FDA labeling, Open Targets, ChEMBL, EuropePMC, etc.).

Given a clinical question, the model performs multi-step tool calls, synthesises the evidence, and returns a free-form answer grounded in authoritative biomedical sources.

Quick start

The model is exposed through the athena-r1 Python package, which handles the tool-call protocol and conversation management. Two services back the agent: vLLM (model server) and ToolUniverse (tool server).

# 1. Install
pip install "athena-r1[vllm,web] @ git+https://github.com/mims-harvard/ATHENA.git"

# 2. Start backing services
bash scripts/launch_tooluniverse.sh         # → :8080
bash scripts/launch_vllm.sh 8000 mims-harvard/ATHENA-R1-Qwen3-8B

# 3. Run the agent (Python)
python -c "
from athena_r1 import AthenaR1
agent = AthenaR1(
    model='mims-harvard/ATHENA-R1-Qwen3-8B',
    vllm_url='http://0.0.0.0:8000/v1',
    tool_server='http://0.0.0.0:8080',
)
print(agent.answer('Dose adjustment for metformin in CKD eGFR 35?').answer)
"

For a chat UI (bundled browser demo with live-streamed reasoning):

python web/agui_server.py    # → http://localhost:8090/ (AG-UI server + demo)

For an OpenAI-compatible API endpoint:

python web/openai_server.py    # → http://localhost:9000/v1/chat/completions

Inference settings (paper-canonical)

Parameter	Value
temperature	0.7
top_p	0.95
top_k	20
min_p	0.0
presence_penalty	0
max_round	40
concurrent Qs	4

Evaluation

Open-ended setting: each question is answered free-form, then mapped to one of the original answer choices.

Benchmark	n	ATHENA-R1	GPT-5
DrugPC (open-ended drug reasoning)	3,168	94.7%	76.9%
TreatmentPC (patient-specific treatment)	456	82.9%	72.2%

ATHENA-R1 exceeds GPT-5 by 17.8 points on DrugPC and 10.7 on TreatmentPC.

See the docs/eval_results.md file in the code repo for the full benchmark tables and the two-level self-learning ablation.

How it works

Stage 1 — multi-step tool reasoning: the model emits <tool_call>...</tool_call> blocks; the runtime dispatches them through ToolUniverse, appends results to the conversation, and re-prompts. Loop continues until [FinalAnswer] or max_round is hit.
Stage 2 (eval only) — option mapping: a separate function call maps the free-form answer to an MCQ letter. Two backends supported: the local ATHENA-R1 model (self-extraction) or Azure GPT-5 (external reader).

Intended use

ATHENA-R1 is a research artifact for treatment-reasoning research and decision support. It is not a medical device and must not be used for direct patient care.

Citation

@article{gao2026athena,
  title   = {An AI agent for treatment reasoning over a biomedical tool universe},
  author  = {Gao, Shanghua and ... and Zitnik, Marinka},
  journal = {arXiv preprint},
  year    = {2026}
}

License

MIT.

Acknowledgements

Evidence retrieval is powered by ToolUniverse, a library of curated biomedical tools.

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