llama-3.2-1B-Mongo-query-generator/README.md

base_model, tags, license, language, datasets, pipeline_tag, library_name

base_model	tags	license	language	datasets	pipeline_tag	library_name
unsloth/Llama-3.2-1B-Instruct	text-generation mongodb query-generation transformers unsloth llama trl gguf quantized	apache-2.0	en	skshmjn/mongo_prompt_query	text-generation	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
• 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.

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📌 Example Usage (Transformers)

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)