56 lines
1.8 KiB
Markdown
56 lines
1.8 KiB
Markdown
---
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base_model: unsloth/Llama-3.2-1B-Instruct
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tags:
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- text-generation
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- mongodb
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- query-generation
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- transformers
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- unsloth
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- llama
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- trl
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- gguf
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- quantized
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license: apache-2.0
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language:
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- en
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datasets:
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- skshmjn/mongo_prompt_query
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pipeline_tag: text-generation
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library_name: transformers
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---
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# MongoDB Query Generator - Llama-3.2-1B (Fine-tuned)
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- **Developed by:** skshmjn
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- **License:** apache-2.0
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- **Finetuned from model:** [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct)
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- **Dataset Used:** [skshmjn/mongodb-chat-query](https://huggingface.co/datasets/skshmjn/mongodb-chat-query)
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- **Supports:** Transformers & GGUF (for fast inference on CPU/GPU)
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## 🚀 **Model Overview**
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This model is designed to **generate MongoDB queries** from natural language prompts. It supports:
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- **Basic CRUD operations:** `find`, `insert`, `update`, `delete`
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- **Aggregation Pipelines:** `$group`, `$match`, `$lookup`, `$sort`, etc.
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- **Indexing & Performance Queries**
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- **Nested Queries & Joins (`$lookup`)**
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Trained using **Unsloth** for efficient fine-tuning and **GGUF quantization** for fast inference.
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---
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## 📌 **Example Usage (Transformers)**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "skshmjn/Llama-3.2-1B-Mongo-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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schema = {} # Pass your mongodb schema here, leave empty for generic queries. Sample available in hugging face's repository
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prompt = "Here is mongodb schema {schema} and Find all employees older than 30 in the 'employees' collection."
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inputs = tokenizer(prompt, return_tensors="pt")
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output = model.generate(**inputs, max_length=100)
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query = tokenizer.decode(output[0], skip_special_tokens=True)
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print(query) |