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Model: khazarai/Med-o1-1.7B
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- sft
+- unsloth
+- medical
+- clinical
+license: apache-2.0
+datasets:
+- mattwesney/CoT_Medical_Diagnosis
+language:
+- en
+base_model:
+- unsloth/Qwen3-1.7B
+pipeline_tag: text-generation
+---
+
+# Model Card for Med-o1-1.7B
+
+## Model Details
+
+Med-o1-1.7B is fine-tuned specifically for medical diagnostic reasoning. Using the CoT_Medical_Diagnosis dataset, the model has learned to not only provide medical diagnoses but also to explain the step-by-step clinical reasoning that leads to its conclusions.
+
+Key features of Med-o1-1.7B include:
+
+- Chain-of-Thought (CoT) reasoning: Generates transparent and structured reasoning for diagnostic decisions.
+- Clinical logic and evidence synthesis: Mimics human-style differential diagnosis and evaluates patient information systematically.
+- Medical domain specialization: Focused entirely on clinical scenarios, from symptom analysis to medical history interpretation.
+- Trust and explainability: Designed to build confidence in AI-driven medical assistance by clearly showing how conclusions are reached.
+
+This model is ideal for researchers, educators, and developers aiming to study, demonstrate, or integrate AI-assisted medical reasoning.
+
+
+## Uses
+
+### Intended Use
+
+- Educational purposes: Teaching clinical reasoning and differential diagnosis.
+- Research applications: Exploring AI in medical decision support and diagnostic logic.
+- Prototyping healthcare AI tools: Generating interpretable diagnostic reasoning.
+
+⚠️ Important: This model is not intended for actual medical diagnosis or treatment decisions. Outputs should not be relied upon as a substitute for professional medical judgment. Always consult licensed healthcare professionals.
+
+
+## Bias, Risks, and Limitations
+
+- Not a substitute for professional medical advice or diagnosis
+- Trained on a limited dataset (3000+ cases); performance may vary with novel or complex clinical scenarios
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("khazarai/Med-o1-1.7B")
+model = AutoModelForCausalLM.from_pretrained(
+    "khazarai/Med-o1-1.7B",
+    device_map={"": 0}
+)
+
+question = """
+Explain the physiological significance of a high hematocrit level, the common medical term used to describe this condition, and list three potential underlying causes.
+"""
+
+messages = [
+    {"role" : "user", "content" : question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+    enable_thinking = True,
+)
+
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 2048,
+    temperature = 0.6,
+    top_p = 0.95,
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+
+## Training Details
+
+### Training Data
+
+The model was fine-tuned on the [moremilk/CoT_Medical_Diagnosis](https://huggingface.co/datasets/moremilk/CoT_Medical_Diagnosis) dataset:
+
+- Over 3007 detailed medical scenarios
+- Each entry includes: patient symptoms, history, reasoning steps (CoT), and final diagnosis
+- Scenarios cover a wide range of clinical cases, ensuring broad exposure to medical reasoning patterns
+
+The dataset emphasizes transparent reasoning, helping the model learn to articulate logical steps for arriving at conclusions.