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--- |
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library_name: transformers |
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pipeline_tag: text-generation |
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base_model: SmolAI/SmolLM2-1.7B |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- smolllm2 |
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- finetuned |
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- medical |
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- homework |
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model_type: causal-lm |
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--- |
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# Medical_Homework2 — Fine-Tuned SmolLM2-1.7B for Medical Reasoning |
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Medical_Homework2 is a fine-tuned version of SmolAI/SmolLM2-1.7B, trained specifically on structured medical question-answer data and short reasoning tasks. |
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The model aims to provide concise, accurate, and educational medical explanations suitable for students and basic learning purposes. |
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--- |
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## Model Overview |
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This model is optimized for medical comprehension tasks such as: |
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- Short medical answers |
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- Step-by-step reasoning |
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- Explanations of conditions, symptoms, and basic physiology |
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- Educational or homework-style responses |
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It is not designed for professional medical diagnosis or treatment decisions. |
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--- |
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## Intended Use |
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### Recommended Use Cases |
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- Medical homework and assignment assistance |
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- Explanation of medical concepts in simple language |
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- Introductory physiology and pathology topics |
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- Basic reasoning about medical questions |
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### Not Recommended |
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- Real-world clinical decision-making |
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- Emergency or diagnostic use |
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- Any situation requiring professional medical judgement |
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--- |
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## Training Data |
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The model was fine-tuned using: |
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- Synthetic medical question-answer pairs |
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- Simplified educational medical explanations |
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- Instruction-answer examples |
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- Homework-style reasoning data |
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No real patient data or clinical records were used. |
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--- |
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## Training Details |
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- Base model: SmolAI/SmolLM2-1.7B |
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- Fine-tuning objective: Causal language modeling |
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- Method: Full or LoRA fine-tuning (depending on your actual setup) |
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- Optimizer: AdamW |
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- Typical epochs: 1–3 |
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If you want, a full training script section can be added. |
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--- |
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## Usage Example |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model_name = "Abeersherif/Medical_Homework2" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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prompt = "Explain what type 2 diabetes is in simple terms." |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=150, |
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temperature=0.7, |
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top_p=0.9, |
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) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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