Create README.md

README.md

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+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - medical
+  - llama
+  - finetuned
+  - health
+model_type: llama
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+---
+
+# Model Card for HealthGPT-TinyLlama
+
+This model is a fine-tuned version of TinyLlama-1.1B-Chat-v1.0 on a custom medical dataset. It was developed to serve as a lightweight, domain-specific assistant capable of answering medical questions fluently and coherently.
+
+## Model Details
+
+### Model Description
+
+HealthGPT-TinyLlama is a 1.1B parameter model fine-tuned using LoRA adapters for the task of medical question answering. The base model is TinyLlama, a compact transformer architecture optimized for performance and efficiency.
+
+* **Developed by:** Selina Zarzour
+* **Shared by:** selinazarzour
+* **Model type:** Causal Language Model
+* **Language(s):** English
+* **License:** apache-2.0
+* **Finetuned from model:** TinyLlama/TinyLlama-1.1B-Chat-v1.0
+
+### Model Sources
+
+* **Repository:** [https://huggingface.co/selinazarzour/healthgpt-tinyllama](https://huggingface.co/selinazarzour/healthgpt-tinyllama)
+* **Demo (local only):** Gradio app tested locally with GPU (not deployed to Spaces due to lack of CPU compatibility)
+
+## Uses
+
+### Direct Use
+
+* Designed to answer general medical questions.
+* Intended for educational and experimental use.
+
+### Out-of-Scope Use
+
+* Not suitable for clinical decision-making or professional diagnosis.
+* Should not be relied on for life-critical use cases.
+
+## Bias, Risks, and Limitations
+
+* The model may hallucinate or provide medically inaccurate information.
+* It has not been validated against real-world clinical data.
+* Biases present in the training dataset may persist.
+
+### Recommendations
+
+* Always verify model outputs with qualified professionals.
+* Do not use in scenarios where safety is critical.
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("selinazarzour/healthgpt-tinyllama")
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+
+prompt = "### Question:\nWhat are the symptoms of diabetes?\n\n### Answer:\n"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=150)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Training Details
+
+### Training Data
+
+* Finetuned on a synthetic dataset composed of medical questions and answers derived from reliable medical knowledge sources.
+
+### Training Procedure
+
+* LoRA adapter training using HuggingFace PEFT and `transformers`
+* Model merged with base weights after training
+
+#### Training Hyperparameters
+
+* Precision: float16 mixed precision
+* Epochs: 3
+* Optimizer: AdamW
+* Batch size: 4
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+* Testing done manually by querying the model with unseen questions.
+* Sample outputs evaluated for relevance, grammar, and factual accuracy.
+
+### Results
+
+* The model produces relevant and coherent answers in most cases.
+* Model performs best on short, fact-based questions.
+
+## Model Examination
+
+Screenshot of local Gradio app interface:
+
+**Note:** The model was not deployed publicly due to GPU-only compatibility, but it runs successfully in local environments with GPU access.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6666f9ad7b3e504d5c14d859/LgPcXfCt7ALzwctwA2iwP.png)
+
+## Environmental Impact
+
+* **Hardware Type:** Google Colab GPU (T4/A100)
+* **Hours used:** \~3 hours
+* **Cloud Provider:** Google Cloud via Colab
+* **Compute Region:** US (unknown exact zone)
+* **Carbon Emitted:** Unknown
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+* LlamaForCausalLM with 22 layers, 32 attention heads, 2048 hidden size
+* LoRA finetuning applied to attention layers only
+
+### Compute Infrastructure
+
+* **Hardware:** Colab GPU
+* **Software:**
+
+  * transformers 4.39+
+  * peft
+  * bitsandbytes (for initial quantized training)
+
+## Citation
+
+**APA:**
+Zarzour, S. (2025). HealthGPT-TinyLlama: A fine-tuned 1.1B LLM for medical Q\&A.
+
+## Model Card Contact
+
+* **Contact:** Selina Zarzour via Hugging Face (@selinazarzour)
+
+---
+
+**Note**: This model is a prototype and not intended for clinical use.