Update README.md

README.md

@@ -1,21 +1,154 @@
 ---
-base_model: unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- gemma4
 license: apache-2.0
 language:
 - en
+base_model:
+- unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
+tags:
+- transformers
+- safetensors
+- gemma4
+- image-text-to-text
+- text-generation-inference
+- unsloth
+- conversational
+- icd-coding
+- clinical-nlp
+- medical-ai
+pipeline_tag: image-text-to-text
 ---
 
-# Uploaded finetuned  model
+# 🏥 gemma4-e4b-icd-coding
+
+A fine-tuned **Gemma 4 E4B** model for automatic **ICD code prediction** from clinical notes. Given a free-text clinical note, the model outputs the relevant ICD diagnosis codes — streamlining medical billing, documentation, and clinical analytics workflows.
+
+---
+
+## 📌 Model Overview
+
+| Property | Details |
+|---|---|
+| **Base Model** | `unsloth/gemma-4-e4b-it-unsloth-bnb-4bit` |
+| **Fine-tuned by** | [nikhil061307](https://huggingface.co/nikhil061307) |
+| **Task** | Clinical Note → ICD Code Prediction |
+| **Language** | English |
+| **License** | Apache 2.0 |
+| **Training Framework** | [Unsloth](https://github.com/unslothai/unsloth) + HuggingFace TRL |
+
+---
+
+## 🚀 What It Does
+
+Given a clinical note like:
+
+> *"Patient presents with persistent cough, fever, and bilateral infiltrates on chest X-ray. Diagnosed with community-acquired pneumonia."*
+
+The model outputs the appropriate ICD-10 code(s), e.g.:
+
+```
+J18.9 - Pneumonia, unspecified organism
+```
+
+---
+
+## 💻 Usage
+
+### Installation
+
+```bash
+pip install unsloth transformers torch
+```
+
+### Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForImageTextToText
+import torch
 
-- **Developed by:** nikhil061307
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
+model_id = "nikhil061307/gemma4-e4b-icd-coding"
 
-This gemma4 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+clinical_note = """
+Patient is a 65-year-old male with a history of type 2 diabetes presenting 
+with polyuria, polydipsia, and HbA1c of 9.2%. Blood glucose fasting at 210 mg/dL.
+"""
+
+messages = [
+    {
+        "role": "user",
+        "content": f"Predict the ICD-10 codes for the following clinical note:\n\n{clinical_note}"
+    }
+]
+
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        temperature=0.1,
+        do_sample=True,
+    )
+
+response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+print(response)
+```
+
+---
+
+## 🧪 Example Input / Output
+
+**Input (Clinical Note):**
+```
+A 52-year-old woman presents with sharp chest pain radiating to the left arm,
+diaphoresis, and shortness of breath. ECG shows ST elevation in leads II, III, aVF.
+Troponin elevated. Impression: Acute inferior STEMI.
+```
+
+**Output (ICD Codes):**
+```
+I21.19 - ST elevation (STEMI) myocardial infarction involving other coronary artery
+```
+
+---
+
+## ⚙️ Training Details
+
+- **Base model:** `unsloth/gemma-4-e4b-it-unsloth-bnb-4bit` (4-bit quantized)
+- **Training speedup:** 2x faster training with [Unsloth](https://github.com/unslothai/unsloth)
+- **Library:** HuggingFace TRL (SFTTrainer)
+- **Quantization:** BnB 4-bit (inference efficient)
+
+---
+
+## ⚠️ Limitations & Disclaimer
+
+- This model is intended for **research and assistive purposes only**.
+- It is **not a substitute for professional medical coding** by certified coders (CPC/CCS).
+- Always verify predicted ICD codes with qualified clinical staff before use in billing or official documentation.
+- Model performance may vary across specialties, note styles, and rare diagnosis categories.
+
+---
+
+## 📄 License
+
+This model is released under the **Apache 2.0** license. See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.
+
+---
+
+## 🙏 Acknowledgements
+
+- [Unsloth AI](https://github.com/unslothai/unsloth) — for the blazing fast fine-tuning framework
+- [Google DeepMind](https://deepmind.google/) — for the Gemma model family
+- [HuggingFace TRL](https://github.com/huggingface/trl) — for the SFT training utilities
+
+---
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+*Made with ❤️ using [Unsloth](https://github.com/unslothai/unsloth)*