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--- |
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library_name: transformers |
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tags: [fine-tuning, medical, oncology, reasoning, chain-of-thought, Qwen-1.5B] |
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--- |
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# Model Card for Medical Oncology Reasoning Fine-Tuned Model |
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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. |
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## Model Details |
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### Model Description |
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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. |
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- **Developed by:** Arihant Tripathi |
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- **Model type:** Causal Language Model (LLM) |
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- **Language(s):** English |
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- **Finetuned from model:** [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) |
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### Model Sources |
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- **Repository:** [EndOfLe/OncoFineTuned](https://huggingface.co/EndOfLe/OncoFineTuned) |
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## Uses |
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### Direct Use |
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This model can be used directly to generate detailed, step-by-step medical reasoning in response to clinical queries. It is especially useful for: |
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- Medical diagnosis support. |
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- Clinical reasoning and treatment planning. |
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- Medical education and research. |
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### Downstream Use |
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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. |
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### Out-of-Scope Use |
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The model is not intended for: |
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- Replacing expert medical advice or making final clinical decisions. |
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- General-purpose language generation without domain adaptation. |
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- High-stakes applications where errors could have severe consequences without expert oversight. |
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## Bias, Risks, and Limitations |
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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: |
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- The model may generate outdated or incorrect medical information. |
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- The reasoning is based on the training data and might reflect its inherent biases. |
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- Use in clinical settings should always involve human review and validation. |
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### Recommendations |
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Users should verify the model’s outputs with expert medical knowledge and ensure its use complies with clinical standards and ethical guidelines. |
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## How to Get Started with the Model |
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You can get started with the model using the following code snippet: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "EndOfLe/OncoFineTuned" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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input_text = "What are the causes of colon cancer?" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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