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Model: skshmjn/llama-3.2-1B-Mongo-query-generator
Source: Original Platform

README.md

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+---
+base_model: unsloth/Llama-3.2-1B-Instruct
+tags:
+- 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
+---
+
+# MongoDB Query Generator - Llama-3.2-1B (Fine-tuned)  
+
+- **Developed by:** skshmjn  
+- **License:** apache-2.0  
+- **Finetuned from model:** [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct)  
+- **Dataset Used:** [skshmjn/mongodb-chat-query](https://huggingface.co/datasets/skshmjn/mongodb-chat-query)  
+- **Supports:** Transformers & GGUF (for fast inference on CPU/GPU)  
+
+## 🚀 **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`)**  
+
+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-3.2-1B-Mongo-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+schema = {} # Pass your mongodb schema here, leave empty for generic queries. Sample available in hugging face's repository
+
+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)