Update README.md

README.md

@@ -10,12 +10,65 @@ language:
 - en
 ---
 
-# Uploaded finetuned  model
+# 🚀 Fine-tuned Gemma 3 Model (4B, 4-bit) by Webkul
 
-- **Developed by:** webkul
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
+This repository contains a fine-tuned version of [Unsloth's](https://github.com/unslothai/unsloth) `gemma-3-4b-it` model, optimized for lightweight 4-bit inference and instruction tuning using Hugging Face's [TRL](https://github.com/huggingface/trl) and Unsloth's speed-optimized framework.
 
-This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+---
+
+## 🔧 Model Details
+
+- **Base Model:** [`unsloth/gemma-3-4b-it-unsloth-bnb-4bit`](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit)
+- **Fine-tuned By:** [Webkul](https://webkul.com)
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+- **Language:** English (`en`)
+- **Model Size:** 4B parameters (4-bit quantized)
+- **Frameworks Used:** Unsloth, Hugging Face Transformers, TRL
+
+---
+
+## 📚 Fine-tuning Dataset
+
+This model was fine-tuned on unopim dev documentation available at [https://devdocs.unopim.com/](https://devdocs.unopim.com/), focusing on structured software documentation and developer support content.
+
+---
+
+## 💡 Intended Use
+
+- Conversational AI assistants trained on UnoPIM developer docs
+- API documentation question answering
+- Developer tools and chatbot integrations
+- Contextual helpdesk or onboarding bots for UnoPIM products
+
+---
+
+## 🧪 How to Use
+
+You can use this model with the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "webkul/gemma-3-4b-it-unopim-docs"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+input_text = "How do I integrate the UnoPIM API for product syncing?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=300)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+⚡ Performance & Efficiency
+Thanks to Unsloth's optimizations, this model trains and runs inference 2x faster with lower memory requirements via 4-bit quantization—ideal for local and edge deployment scenarios.
+
+❤️ Acknowledgments
+<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
+
+Special thanks to the Unsloth team and Hugging Face for enabling fast, low-resource fine-tuning of large language models.
+
+📄 License
+This model is licensed under the Apache License 2.0.
+
+---
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)