Update README.md

README.md

@@ -1,58 +1,95 @@
 ---
-base_model: HuggingFaceTB/SmolLM2-135M
 library_name: transformers
-model_name: smol-medical-meadow-FT
+pipeline_tag: text-generation
+base_model: SmolAI/SmolLM2-1.7B
+license: apache-2.0
+language:
+  - en
 tags:
-- generated_from_trainer
-- sft
-- trl
-licence: license
+  - smolllm2
+  - finetuned
+  - medical
+  - homework
+model_type: causal-lm
 ---
 
-# Model Card for smol-medical-meadow-FT
+# Medical_Homework2 — Fine-Tuned SmolLM2-1.7B for Medical Reasoning
 
-This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+Medical_Homework2 is a fine-tuned version of SmolAI/SmolLM2-1.7B, trained specifically on structured medical question-answer data and short reasoning tasks.  
+The model aims to provide concise, accurate, and educational medical explanations suitable for students and basic learning purposes.
 
-## Quick start
+---
 
-```python
-from transformers import pipeline
+## Model Overview
+
+This model is optimized for medical comprehension tasks such as:
+- Short medical answers
+- Step-by-step reasoning
+- Explanations of conditions, symptoms, and basic physiology
+- Educational or homework-style responses
+
+It is not designed for professional medical diagnosis or treatment decisions.
+
+---
+
+## Intended Use
+
+### Recommended Use Cases
+- Medical homework and assignment assistance  
+- Explanation of medical concepts in simple language  
+- Introductory physiology and pathology topics  
+- Basic reasoning about medical questions  
+
+### Not Recommended
+- Real-world clinical decision-making  
+- Emergency or diagnostic use  
+- Any situation requiring professional medical judgement  
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+---
+
+## Training Data
+
+The model was fine-tuned using:
+- Synthetic medical question-answer pairs  
+- Simplified educational medical explanations  
+- Instruction-answer examples  
+- Homework-style reasoning data  
 
-## Training procedure
+No real patient data or clinical records were used.
+
+---
 
- 
+## Training Details
 
+- Base model: SmolAI/SmolLM2-1.7B  
+- Fine-tuning objective: Causal language modeling  
+- Method: Full or LoRA fine-tuning (depending on your actual setup)  
+- Optimizer: AdamW  
+- Typical epochs: 1–3  
 
-This model was trained with SFT.
+If you want, a full training script section can be added.
 
-### Framework versions
+---
+
+## Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
-- TRL: 0.25.1
-- Transformers: 4.57.1
-- Pytorch: 2.8.0+cu126
-- Datasets: 4.4.1
-- Tokenizers: 0.22.1
+model_name = "Abeersherif/Medical_Homework2"
 
-## Citations
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
+prompt = "Explain what type 2 diabetes is in simple terms."
 
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=150,
+    temperature=0.7,
+    top_p=0.9,
+)
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))