DeepSeek-R1-Medical-CoT Open-source Medical Reasoning Model - Enhance Reasoning Ability for Medical Questions

Deepseek R1 Medical CoT

Developed by zijiechen156

A medical chain-of-thought reasoning model fine-tuned from DeepSeek-R1-Distill-Llama-8B, enhanced with QLoRA quantization and Unsloth optimization for medical reasoning.

Large Language Model

Safetensors

Open Source License:MIT #Medical Reasoning #Chain-of-Thought Optimization #QLoRA Fine-tuning

Downloads 49

Release Time : 3/3/2025

Model Overview

A fine-tuned model optimized for medical chain-of-thought (CoT) reasoning, suitable for medical QA and reasoning tasks.

Model Features

Medical Chain-of-Thought Reasoning Optimization

Specifically optimized for medical chain-of-thought (CoT) prompting to enhance reasoning capabilities for complex medical problems.

Efficient Fine-Tuning Techniques

Utilizes QLoRA quantization and Unsloth optimization to achieve efficient fine-tuning with limited hardware resources.

Medical Terminology Support

Specially optimized for understanding and generating medical terminology.

Model Capabilities

Medical QA

Chain-of-Thought Reasoning

Medical Terminology Processing

Use Cases

Medical Consultation

Symptom Analysis

Analyze early symptoms described by patients and provide possible disease inferences.

Treatment Plan Suggestions

Provide preliminary treatment recommendations based on patient conditions.

Medical Education

Medical Terminology Explanation

Explain complex medical terms and concepts to medical students.

🚀 DeepSeek-R1-Medical-CoT

Fine-tuning DeepSeek R1 for Medical Chain-of-Thought Reasoning

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Llama-8B, specifically designed to enhance medical reasoning through Chain-of-Thought (CoT) prompting. It is trained using QLoRA with Unsloth optimization, allowing efficient fine-tuning on limited hardware resources.

🚀 Quick Start

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_name = "your-huggingface-username/DeepSeek-R1-Medical-CoT"

tokenizer = AutoTokenizer.from_pretrained(repo_name)
model = AutoModelForCausalLM.from_pretrained(repo_name)

model.eval()

Advanced Usage

prompt = "What are the early symptoms of diabetes?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=200)

response = tokenizer.decode(output[0], skip_special_tokens=True)
print("Model Response:", response)

📚 Documentation

✨ Features

This model is fine - tuned from deepseek-ai/DeepSeek-R1-Distill-Llama-8B for enhanced medical reasoning.
It uses Chain - of - Thought (CoT) prompting for better medical reasoning.
Trained with QLoRA and Unsloth optimization for efficient fine - tuning on limited hardware.

📋 Model Details

Model Information

Property	Details
Model Type	Fine - tuned version of `deepseek-ai/DeepSeek-R1-Distill-Llama-8B`
Developed by	[Your Name or Organization]
Language(s)	English, with a focus on medical terminology
Training Data	Medical reasoning dataset (`medical-o1-reasoning-SFT`)
Fine - tuning Method	QLoRA (4 - bit adapters), later merged into 16 - bit weights
Optimization	Unsloth (2x faster fine - tuning with lower memory usage)

Model Sources

Property	Details
Repository	[Your Hugging Face Model Repo URL]
Paper (if applicable)	[Link]
Demo (if applicable)	[Link]

📢 Acknowledgments

DeepSeek-AI for releasing DeepSeek-R1
Unsloth for optimized LoRA fine - tuning
Hugging Face for hosting the models

📄 License

This model is released under the MIT license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご