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skshmjn/llama-3.2-3B-Mongo-query-generator

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

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
—Dataset Used: 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-3.2-3B-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)