---
library_name: transformers
tags: [fine-tuning, medical, oncology, reasoning, chain-of-thought, Qwen-1.5B]
---

# Model Card for Medical Oncology Reasoning Fine-Tuned Model

This is a fine-tuned version of the **DeepSeek-R1-Distill-Qwen-1.5B** model, specifically adapted for medical oncology reasoning tasks using chain-of-thought prompting. The fine-tuning was performed on a curated subset of the [FreedomIntelligence/medical-o1-reasoning-SFT](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT) dataset. This model is designed to provide detailed, step-by-step reasoning when answering medical questions, making it suitable for clinical decision support, medical education, and research.

## Model Details

### Model Description

This model leverages the capabilities of **DeepSeek-R1-Distill-Qwen-1.5B** and has been fine-tuned to enhance its performance in medical reasoning. It incorporates chain-of-thought prompting to produce detailed explanations for clinical queries. The fine-tuning process focused on tasks related to clinical diagnostics, treatment planning, and medical reasoning.

- **Developed by:** Arihant Tripathi
- **Model type:** Causal Language Model (LLM)
- **Language(s):** English
- **Finetuned from model:** [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)

### Model Sources

- **Repository:** [EndOfLe/OncoFineTuned](https://huggingface.co/EndOfLe/OncoFineTuned)

## Uses

### Direct Use

This model can be used directly to generate detailed, step-by-step medical reasoning in response to clinical queries. It is especially useful for:
- Medical diagnosis support.
- Clinical reasoning and treatment planning.
- Medical education and research.

### Downstream Use

The model can be integrated into larger clinical decision support systems or educational tools that require natural language understanding and detailed reasoning for medical queries.

### Out-of-Scope Use

The model is not intended for:
- Replacing expert medical advice or making final clinical decisions.
- General-purpose language generation without domain adaptation.
- High-stakes applications where errors could have severe consequences without expert oversight.

## Bias, Risks, and Limitations

The model’s output should be treated as an aid to human decision-making rather than a substitute for professional medical advice. Key considerations include:
- The model may generate outdated or incorrect medical information.
- The reasoning is based on the training data and might reflect its inherent biases.
- Use in clinical settings should always involve human review and validation.

### Recommendations

Users should verify the model’s outputs with expert medical knowledge and ensure its use complies with clinical standards and ethical guidelines.

## How to Get Started with the Model

You can get started with the model using the following code snippet:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "EndOfLe/OncoFineTuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "What are the causes of colon cancer?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))