Instructions to use mervp/SQLGenie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mervp/SQLGenie with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-3b-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "mervp/SQLGenie") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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**SQLGenie** is a lightweight LoRA adapter fine-tuned on top of Unsloth’s 4-bit LLaMA 3 (3B) model. It is designed to convert natural language instructions into valid SQL queries with minimal compute overhead, making it ideal for integrating into data-driven applications,or chat interfaces.
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it has been trained over 100K types of text based on various different domains such as Education, Technical, Health and more
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## Model
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### Model Description
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**SQLGenie** is a lightweight LoRA adapter fine-tuned on top of Unsloth’s 4-bit LLaMA 3 (3B) model. It is designed to convert natural language instructions into valid SQL queries with minimal compute overhead, making it ideal for integrating into data-driven applications,or chat interfaces.
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it has been trained over 100K types of text based on various different domains such as Education, Technical, Health and more
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## Model Highlights
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- **Base model**: `Llama3 3B`
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- **Tokenizer**: Compatible with `Llama3 3B`
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- **Fine tuned for**: Text to SQL Converter
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- **Accuracy**: > 85%
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- **Language**: English Natural Language Sentences fientuned
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- **Format**: `safetensors`
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### Model Description
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