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vitals-interpreter-model

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+# Vitals Interpreter Model (Fine-Tuned LLM)
+## Project Overview
+This project implements a fine-tuned transformer model that interprets basic human vital signs and generates structured health guidance.
+The model takes numerical vitals as input and produces a concise, human-readable output consisting of:
+- Health status classification
+- Suggested action/advice
+---
+##  Objective
+To build a lightweight, efficient AI system that:
+- Understands structured vital inputs
+- Classifies health condition into categories
+- Generates consistent and controlled responses
+---
+##  Model Details
+- **Base Model:** t5-small
+- **Architecture:** Encoder-Decoder Transformer
+- **Fine-Tuning Type:** Supervised Fine-Tuning (SFT)
+- **Framework:** Hugging Face Transformers
+---
+##  Input Format
+interpret vitals -> heart rate X, blood pressure Y/Z, temperature T
+### Example:
+interpret vitals -> heart rate 125, blood pressure 150/95, temperature 100
+---
+##  Output Format
+Status: <Normal | High | Low | Critical> | Advice: <short guidance>
+### Example Output:
+Status: High | Advice: Monitor and consult doctor
+---
+##  Dataset
+- **Type:** Synthetic dataset
+- **Size:** ~30–50 samples
+- **Design Approach:**
+  - Based on medically accepted ranges of vital signs
+  - Balanced across categories:
+    - Normal
+    - High
+    - Low
+    - Critical
+### Why Synthetic Data?
+Due to lack of publicly available labeled text datasets for this task, a controlled dataset was generated to:
+- Ensure consistency in output format
+- Improve learning efficiency
+- Avoid noisy or unstructured data
+---
+##  Training Configuration
+- **Epochs:** 20–30
+- **Batch Size:** 2–4
+- **Learning Rate:** 5e-5
+- **Max Sequence Length:** 64
+- **Tokenizer:** AutoTokenizer (T5)
+---
+##  Evaluation
+### Method:
+- Manual testing with unseen inputs
+- Verification of:
+  - Correct classification (Normal / High / Low / Critical)
+  - Proper output structure
+  - Relevance of advice
+### Sample Predictions:
+| Input | Output |
+|------|--------|
+| HR: 125, BP: 150/95, Temp: 100 | Status: High \| Advice: Monitor and consult doctor |
+| HR: 72, BP: 120/80, Temp: 98.6 | Status: Normal \| Advice: No action needed |
+| HR: 140, BP: 170/110, Temp: 103 | Status: Critical \| Advice: Emergency care required |
+---
+##  How to Use
+### Installation
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+Author:
+Archee Sinha
+B.Tech CSE (AI)
+ABES Institute of Technology