Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
+base_model: google/gemma-3-4b-it-unsloth-bnb-4bit
 tags:
 - text-generation-inference
 - transformers
@@ -11,12 +11,61 @@ language:
 - en
 ---
 
-# Uploaded  model
+# 🧠 Fine-Tuning Gemma 3B on Healthcare Admin Tasks
 
-- **Developed by:** xgalaxy
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
+This repository demonstrates how to fine-tune the instruction-tuned [`google/gemma-3-4b-it`](https://huggingface.co/google/gemma-3-4b-it) model on a custom dataset covering administrative tasks in the healthcare industry.
 
-This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+---
+
+## 📌 Project Overview
+
+We use the [Unsloth](https://github.com/unslothai/unsloth) framework to:
+- Load and quantize the base Gemma model in **4-bit precision**.
+- Apply **LoRA (Low-Rank Adaptation)** for efficient parameter tuning.
+- Train the model using Hugging Face's `trl` library and `SFTTrainer`.
+
+This setup significantly reduces memory footprint and training cost, making it suitable for training on consumer GPUs (e.g. Colab, T4, A100).
+
+---
+
+## 🩺 Dataset: Healthcare Admin
+
+- **Source**: [`xgalaxy/healthcare_admin`](https://huggingface.co/datasets/xgalaxy/healthcare_admin)
+- **Format**: ShareGPT-style JSON format with structured `user` and `assistant` roles
+- **Coverage**:
+  - Appointment scheduling, cancellation, and rescheduling
+  - Edge cases involving follow-ups, missing info, and ambiguous requests
+  - Multi-turn conversations to emulate real-world interactions
+
+---
+
+## 🛠️ Key Components
+
+### ✅ Model Setup
+- `google/gemma-3-4b-it` loaded using Unsloth's `FastModel.from_pretrained()`
+- 4-bit quantization enabled via `load_in_4bit=True`
+- LoRA adapters injected for memory-efficient tuning
+
+### ✅ Training
+- Supervised fine-tuning with `SFTTrainer`
+- Batch size simulated using `gradient_accumulation_steps`
+- Linear learning rate scheduler with warmup
+- Training capped at a fixed number of steps for fast iteration
+
+---
+
+## 🚀 Trained Model
+
+The fine-tuned model is available on Hugging Face:
+👉 [xgalaxy/gemma-3](https://huggingface.co/xgalaxy/gemma-3)
+
+---
+
+## 🔗 Resources
+
+- 🔗 [Unsloth GitHub](https://github.com/unslothai/unsloth)
+- 📘 [Gemma on Hugging Face](https://huggingface.co/google/gemma-3-4b-it)
+- 🗃️ [Healthcare Admin Dataset](https://huggingface.co/datasets/xgalaxy/healthcare_admin)
+
+---
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)