Instructions to use TaoMedAI/RareSeek-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TaoMedAI/RareSeek-R1 with Transformers:

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

pipe = pipeline("text-generation", model="TaoMedAI/RareSeek-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TaoMedAI/RareSeek-R1")
model = AutoModelForCausalLM.from_pretrained("TaoMedAI/RareSeek-R1")
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 TaoMedAI/RareSeek-R1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TaoMedAI/RareSeek-R1

SGLang

How to use TaoMedAI/RareSeek-R1 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 "TaoMedAI/RareSeek-R1" \
    --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": "TaoMedAI/RareSeek-R1",
		"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 "TaoMedAI/RareSeek-R1" \
        --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": "TaoMedAI/RareSeek-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TaoMedAI/RareSeek-R1 with Docker Model Runner:
```
docker model run hf.co/TaoMedAI/RareSeek-R1
```

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RareSeek-R1: A specialized language model for rare disease diagnosis and reasoning

RareSeek-R1 is a domain-specialized large language model for rare-disease diagnostic reasoning, developed through a Progressive Parameter-Efficient Transfer Learning framework. The model is first instruction-tuned on the clinically grounded RareMed-Corpus, a large, multi-source dataset deeply integrated from medical textbooks, guidelines, biomedical literature, and real-world EHR narratives. It is then fine-tuned on RareMed-CoT, a high-fidelity corpus designed to instill explicit, stepwise clinical reasoning aligned with real diagnostic workflows. To further enhance factual reliability, GraphRAG is incorporated to anchor the model’s inference to up-to-date variant–gene–phenotype–disease relationships. This retrieval augmentation substantially reduces hallucinations, improves factual calibration, and yields notable performance gains—particularly when EHR narratives are combined with prioritized genetic variants. Together, RareSeek-R1 performs direct reasoning over full-length EHRs, leverages graph-grounded retrieval, and demonstrably augments clinician-level diagnostic accuracy, advancing a reliable and scalable AI paradigm for rare-disease diagnosis.

RareSeek-R1 Teaser Image

RareMedData: https://huggingface.co/datasets/TaoMedAI/RareMedData

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Safetensors

Model size

71B params

Tensor type

BF16

Model tree for TaoMedAI/RareSeek-R1

Base model

deepseek-ai/DeepSeek-R1-Distill-Llama-70B

Finetuned

(19)

this model