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", 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 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
```

RareSeek-R1 / README.md

TaoMedAI

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	---
	license: afl-3.0
	language:
	- en
	- zh
	metrics:
	- accuracy
	base_model:
	- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- medical
	- deepseek-r1
	- health
	- ehr
	- reasoning

	# 核心配置：开启门控
	# gated: true

	# （可选）申请表单上方的提示语
	# extra_gated_heading: "Access Request"
	# extra_gated_description: "Please provide your organization and intended use."

	# （核心）自定义问卷字段
	#extra_gated_fields:
	# Affiliation: text # 单位/机构（文本框，满足你的需求）
	# Research Purpose: text # 研究用途（文本框）
	# Country: text # 国家（文本框）
	---

	<div align="center">
	<h1>🧬 RareSeek-R1</h1>
	<h3>A Specialized Language Model for Rare Disease Diagnosis and Clinical Reasoning</h3>
	</div>

	<p align="center">
	<img src="https://img.shields.io/badge/Language-English%20%7C%20Chinese-blue"> <img src="https://img.shields.io/badge/Task-Clinical_Reasoning-brightgreen">
	<a href="https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B"> <img src="https://img.shields.io/badge/Base_Model-DeepSeek--R1--Distill--Llama--70B-orange"></a>
	<a href="https://opensource.org/licenses/AFL-3.0"><img src="https://img.shields.io/badge/License-AFL--3.0-gray"></a>
	</p>

	## 📖 Model Overview
	RareSeek-R1 is a domain-specialized large language model tailored 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 (Electronic Health Record) narratives. It is subsequently fine-tuned on RareMed-CoT, a high-fidelity corpus designed to instill explicit, stepwise clinical reasoning that aligns with real-world diagnostic workflows.

	<p align="center">
	<img src="https://github.com/mulinlab/RareSeek-R1/raw/main/RareSeek-R1.png" alt="Figure 1: Overall framework and pipeline of RareSeek-R1." width="800">
	<em>Figure 1: Overall framework and pipeline of RareSeek-R1.</em>
	</p>

	## 🗄️ Dataset & Resources
	📚 RareMedData: Access the comprehensive medical dataset used for training here:
	👉 [https://huggingface.co/datasets/TaoMedAI/RareMedData](https://huggingface.co/datasets/TaoMedAI/RareMedData)