Update README.md

README.md

@@ -1,22 +1,56 @@
 ---
 base_model: unsloth/Llama-3.2-3B-Instruct
 tags:
-- text-generation-inference
+- text-generation
+- mongodb
+- query-generation
 - transformers
 - unsloth
 - llama
 - trl
+- gguf
+- quantized
 license: apache-2.0
 language:
 - en
+datasets:
+- skshmjn/mongo_prompt_query
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
-# Uploaded  model
+# MongoDB Query Generator - Llama-3.2-3B (Fine-tuned)  
 
-- **Developed by:** skshmjn
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
+- **Developed by:** skshmjn  
+- **License:** apache-2.0  
+- **Finetuned from model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)  
+- **Dataset Used:** [skshmjn/mongodb-chat-query](https://huggingface.co/datasets/skshmjn/mongodb-chat-query)  
+- **Supports:** Transformers & GGUF (for fast inference on CPU/GPU)  
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## 🚀 **Model Overview**  
+This model is designed to **generate MongoDB queries** from natural language prompts. It supports:  
+- **Basic CRUD operations:** `find`, `insert`, `update`, `delete`  
+- **Aggregation Pipelines:** `$group`, `$match`, `$lookup`, `$sort`, etc.  
+- **Indexing & Performance Queries**  
+- **Nested Queries & Joins (`$lookup`)**  
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+Trained using **Unsloth** for efficient fine-tuning and **GGUF quantization** for fast inference.  
+
+---
+
+## 📌 **Example Usage (Transformers)**  
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "skshmjn/Llama-3.2-3B-Mongo-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+schema = {} // Pass your mongodb schema here or leave empty for generic queries 
+
+prompt = "Here is mongodb schema {schema} and Find all employees older than 30 in the 'employees' collection."
+inputs = tokenizer(prompt, return_tensors="pt")
+
+output = model.generate(**inputs, max_length=100)
+query = tokenizer.decode(output[0], skip_special_tokens=True)
+
+print(query)