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Tanit-MedReason-8B

Text Generation
Transformers
Safetensors
English
qwen3
medical
clinical-reasoning
chain-of-thought
sft
dpo
tanit
conversational
Eval Results (legacy)
text-generation-inference
Model card Files
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  1. .gitattributes +1 -0
  2. README.md +286 -0
  3. chat_template.jinja +14 -0
  4. config.json +74 -0
  5. dataset_info.json +34 -0
  6. generation_config.json +10 -0
  7. model-00001-of-00004.safetensors +3 -0
  8. model-00002-of-00004.safetensors +3 -0
  9. model-00003-of-00004.safetensors +3 -0
  10. model-00004-of-00004.safetensors +3 -0
  11. model.safetensors.index.json +407 -0
  12. special_tokens_map.json +23 -0
  13. tokenizer.json +3 -0
  14. tokenizer_config.json +247 -0
  15. training_args.bin +3 -0
  16. training_metrics.json +38 -0
.gitattributes CHANGED
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ tags:
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+ - medical
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+ - healthcare
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+ - reasoning
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+ - llm
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+ - deepseek
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+ - qwen
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+ - tanit
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+ base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
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+ model-index:
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+ - name: Tanit-MedReason-8B
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+ results:
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+ - task:
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+ type: question-answering
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+ name: Medical QA
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+ metrics:
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+ - type: accuracy
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+ value: 60.3
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+ name: Test Accuracy
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+ ---
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+
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+ <div align="center">
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+
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+ # 🏥 Tanit-MedReason-8B
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+
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+ ### Advanced Medical Reasoning Model by TANIT Healthcare Technologies
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+
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+ [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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+ [![Model](https://img.shields.io/badge/Model-8B%20Parameters-green.svg)]()
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+ [![Phase](https://img.shields.io/badge/Phase-Final%20(v1.0)-orange.svg)]()
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+
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+ </div>
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+
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+ ---
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+
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+ ## 🌟 Model Overview
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+
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+ **Tanit-MedReason-8B** is a specialized medical reasoning model developed by [TANIT Healthcare Technologies](https://tanitai.com).
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+
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+ Production-ready medical reasoning model trained through a 4-phase curriculum: Broad SFT → Reasoning SFT → DPO Alignment → MedReason Chain-of-Thought fine-tuning. Achieves strong performance across medical benchmarks with transparent reasoning via <think> tags.
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+
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+ ### Key Features
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+
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+ - 🧠 **Advanced Medical Reasoning**: Deep chain-of-thought reasoning for complex medical scenarios
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+ - 🔬 **Evidence-Based Responses**: Trained on validated medical knowledge sources
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+ - 💭 **Transparent Thinking**: Exposes reasoning process via `<think>` tags
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+ - ⚡ **Efficient**: 8B parameters optimized for deployment
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+ - 🛡️ **Safety-Aligned**: DPO-trained for safer, more helpful responses
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+
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+
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+ ## 🔬 Training Pipeline
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+
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+ Tanit-MedReason-8B was developed through a carefully designed 4-phase training pipeline:
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+
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+ ```
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+ ┌─────────────────────────────────────────────────────────────────┐
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+ │ Phase 1: Broad Medical SFT (24.5 hours) │
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+ │ ├── 800K+ diverse medical samples │
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+ │ ├── Foundation knowledge acquisition │
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+ │ └── Loss: 1.129 → Comprehensive medical understanding │
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+ ├─────────────────────────────────────────────────────────────────┤
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+ │ Phase 2: Reasoning-Focused SFT (6.8 hours) │
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+ │ ├── 155K high-quality reasoning chains │
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+ │ ├── Focus: Medical-R1-Distill + Huatuo-o1 │
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+ │ └── Loss: 1.014 → Enhanced reasoning capabilities │
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+ ├─────────────────────────────────────────────────────────────────┤
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+ │ Phase 3A: DPO Preference Alignment (5.9 hours) │
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+ │ ├── 33K preference pairs (FineMed-DPO) │
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+ │ ├── Direct Preference Optimization │
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+ │ └── Loss: 0.263 → Human-aligned responses │
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+ ├─────────────────────────────────────────────────────────────────┤
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+ │ Phase 4: MedReason CoT Fine-Tuning (3 hours) │
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+ │ ├── 35K samples from UCSC-VLAA/MedReason │
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+ │ ├── Structured medical reasoning with <think> tags │
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+ │ └── Loss: 1.662 → Production-ready model │
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+ └─────────────────────────────────────────────────────────────────┘
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+ ```
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+
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+ **Total Training Time**: ~42 hours on NVIDIA H200 144GB
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+
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+
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+ ## 🚀 Quick Start
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+
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+ ### Installation
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+
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+ ```bash
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+ pip install transformers torch accelerate
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+ ```
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+
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+ ### Using with Transformers
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+
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+ model_name = "TanitAI/Tanit-MedReason-8B"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_name,
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto",
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+ trust_remote_code=True
110
+ )
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+
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+ # Medical question
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+ question = """A 45-year-old male presents with sudden onset chest pain radiating to
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+ the left arm, diaphoresis, and shortness of breath. ECG shows ST elevation in
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+ leads V1-V4. What is the most likely diagnosis and immediate management?"""
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+
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+ messages = [
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+ {"role": "user", "content": question}
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+ ]
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+
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+ # Apply chat template
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+ input_text = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
126
+ )
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+
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+ inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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+
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+ # Generate response
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+ with torch.no_grad():
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+ outputs = model.generate(
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+ **inputs,
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+ max_new_tokens=2048,
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+ temperature=0.6,
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+ top_p=0.95,
137
+ do_sample=True
138
+ )
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+
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+ response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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+ print(response)
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+ ```
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+
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+ ### Using with vLLM (Recommended for Production)
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+
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+ ```python
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+ from vllm import LLM, SamplingParams
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+
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+ model_name = "TanitAI/Tanit-MedReason-8B"
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+
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+ llm = LLM(
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+ model=model_name,
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+ dtype="bfloat16",
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+ tensor_parallel_size=1, # Adjust based on your GPU setup
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+ trust_remote_code=True,
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+ max_model_len=8192
157
+ )
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+
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+ sampling_params = SamplingParams(
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+ temperature=0.6,
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+ top_p=0.95,
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+ max_tokens=2048
163
+ )
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+
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+ question = "What are the diagnostic criteria for Type 2 Diabetes Mellitus?"
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+
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+ # Format with chat template
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+ prompt = f"<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"
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+
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+ outputs = llm.generate([prompt], sampling_params)
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+ print(outputs[0].outputs[0].text)
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+ ```
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+
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+ ### Using via API (Without Loading Model)
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+
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+ For team members who need access without loading the model locally, use the HuggingFace Inference API:
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+
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+ ```python
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+ import requests
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+
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+ API_URL = "https://api-inference.huggingface.co/models/TanitAI/Tanit-MedReason-8B"
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+ headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
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+
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+ def query(payload):
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+ response = requests.post(API_URL, headers=headers, json=payload)
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+ return response.json()
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+
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+ # Example query
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+ output = query({
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+ "inputs": "What is the first-line treatment for hypertension?",
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+ "parameters": {
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+ "max_new_tokens": 1024,
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+ "temperature": 0.6
194
+ }
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+ })
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+ print(output)
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+ ```
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+
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+ ### Accessing via HuggingFace Hub
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+
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+ ```python
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+ from huggingface_hub import InferenceClient
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+
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+ client = InferenceClient(
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+ model="TanitAI/Tanit-MedReason-8B",
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+ token="YOUR_HF_TOKEN"
207
+ )
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+
209
+ response = client.text_generation(
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+ "Explain the pathophysiology of heart failure.",
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+ max_new_tokens=1024,
212
+ temperature=0.6
213
+ )
214
+ print(response)
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+ ```
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+
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+ ## 📊 Evaluation Results
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+
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+ | Benchmark | Accuracy |
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+ |-----------|----------|
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+ | MedQA | 60.3% |
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+ | MMLU Medical | 66.4% |
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+ | MedExQA | 62.1% |
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+ | PubMedQA | 74.6% |
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+ | MedMCQA | 52.9% |
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+
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+ *Evaluated using zero-shot prompting with chain-of-thought reasoning.*
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+
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+
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+ ## 📋 Training Details
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+
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+ | Parameter | Value |
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+ |-----------|-------|
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+ | **Base Model** | `deepseek-ai/DeepSeek-R1-0528-Qwen3-8B` |
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+ | **Training Phase** | Final (v1.0) |
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+ | **Training Data** | 35K samples from UCSC-VLAA/MedReason dataset with structured medical reasoning |
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+ | **Training Steps** | 4,442 |
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+ | **Training Time** | 3.0 hours |
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+ | **Final Loss** | 1.662 |
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+ | **Precision** | bfloat16 |
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+ | **Context Length** | 8,192 tokens |
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+
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+ ## ⚠️ Limitations & Intended Use
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+
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+ ### Intended Use
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+ - Medical education and research
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+ - Clinical decision support (with physician oversight)
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+ - Medical question answering
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+ - Healthcare documentation assistance
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+
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+ ### Limitations
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+ - **Not a replacement for professional medical advice**
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+ - May generate plausible-sounding but incorrect information
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+ - Performance varies across medical specialties
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+ - Should always be used with human oversight in clinical settings
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+
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+ ### Ethical Considerations
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+ - This model is intended to assist, not replace, healthcare professionals
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+ - Always verify medical information with authoritative sources
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+ - Do not use for diagnosis or treatment without professional consultation
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+
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+ ## 📜 License
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+
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+ This model is released under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0).
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+
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+ ## 🙏 Acknowledgments
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+
268
+ - Built on [DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B)
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+ - Training data from FreedomIntelligence, UCSC-VLAA, Intelligent-Internet, and other open-source medical datasets
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+ - Developed by [TANIT Healthcare Technologies](https://tanitai.com)
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+
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+ ## 📧 Contact
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+
274
+ For questions, collaborations, or access requests:
275
+ - **Organization**: [TANIT Healthcare Technologies](https://huggingface.co/TanitAI)
276
+ - **Email**: contact@tanitai.com
277
+
278
+ ---
279
+
280
+ <div align="center">
281
+
282
+ **Made with ❤️ by TANIT Healthcare Technologies**
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+
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+ *Advancing healthcare through AI*
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+
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+ </div>
chat_template.jinja ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true, is_last_user=false) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '
2
+
3
+ ' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{% set content = message['content'] %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{%- set ns.is_first = false -%}{%- set ns.is_last_user = true -%}{{'<｜User｜>' + content + '<｜Assistant｜>'}}{%- endif %}{%- if message['role'] == 'assistant' %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{% endif %}{%- if message['role'] == 'assistant' and message['tool_calls'] is defined and message['tool_calls'] is not none %}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{%- endif %}{%- set ns.is_first = false %}{%- set ns.is_tool = false -%}{%- set ns.is_output_first = true %}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if content is none %}{{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
4
+ ' + '```json' + '
5
+ ' + tool['function']['arguments'] + '
6
+ ' + '```' + '<｜tool▁call▁end｜>'}}{%- else %}{{content + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
7
+ ' + '```json' + '
8
+ ' + tool['function']['arguments'] + '
9
+ ' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'
10
+ ' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '
11
+ ' + '```json' + '
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+ ' + tool['function']['arguments'] + '
13
+ ' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- endfor %}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- if message['role'] == 'assistant' and (message['tool_calls'] is not defined or message['tool_calls'] is none)%}{%- set ns.is_last_user = false -%}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + content + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{{content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_last_user = false -%}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'
14
+ <｜tool▁output▁begin｜>' + content + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_last_user and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}
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+ {
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+ "architectures": [
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+ "vocab_size": 151936
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+ }
dataset_info.json ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "total_samples": 35526,
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+ "config": {
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+ "model_path": "./outputs/phase1_phase3a_dpo_alignment",
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+ "output_dir": "./outputs/phase4_medreason_cot",
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+ "max_seq_length": 8192,
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+ "min_reasoning_length": 500,
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+ "learning_rate": 5e-07,
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+ "adam_beta1": 0.9,
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+ "adam_beta2": 0.999,
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+ "adam_epsilon": 1e-08,
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+ "use_curriculum": true,
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+ "save_steps": 200,
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+ "seed": 42,
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+ "bf16": true,
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+ "gradient_checkpointing": true,
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+ "dataloader_num_workers": 4
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+ },
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+ "timestamp": "2026-01-26T18:01:49.960457"
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+ }
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