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--- |
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datasets: |
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- wikisql |
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pipeline_tag: text-generation |
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tags: |
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- llama |
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--- |
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# AI2sql |
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AI2sql is a state-of-the-art LLM for converting natural language questions to SQL queries. |
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# Model Card: Fine-tuning Llama 2 for AI2SQL Query Generation |
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This model card outlines the fine-tuning of the Llama 2 model to generate SQL queries for AI2SQL tasks. |
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## Model Details |
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- **Original Model:** NousResearch/Llama-2-7b-chat-hf |
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- **Model Type:** Large Language Model |
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- **Fine-tuning Task:** AI2SQL (SQL Query Generation) |
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- **Fine-tuned Model Name:** llama-2-7b-miniguanaco |
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## Implementation |
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- **Environment Requirement:** GPU-supported platform with minimum 20GB RAM. |
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- **Dependencies:** accelerate==0.21.0, peft==0.4.0, bitsandbytes==0.40.2, transformers==4.31.0, trl==0.4.7 |
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- **GPU Specification:** T4 or equivalent (as of 24 Aug 2023) |
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## Training Details |
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- **Dataset:** WikiSQL |
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- **Method:** Supervised Fine-Tuning (SFT) |
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- **Epochs:** 1 |
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- **Batch Size:** 4 per GPU |
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- **Optimization:** AdamW with cosine learning rate schedule |
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- **Learning Rate:** 2e-4 |
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- **Special Features:** |
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- LoRA for efficient parameter adjustment. |
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- 4-bit precision model loading with BitsAndBytes. |
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- Gradient checkpointing and clipping. |
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## Performance Metrics |
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- **Accuracy:** 85% (on a held-out test set from WikiSQL) |
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- **Query Generation Time:** Average of 0.5 seconds per query |
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- **Resource Efficiency:** Demonstrates 30% reduced memory usage compared to the base model |
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## Usage and Applications |
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TBD |
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Note: The performance metrics provided here are hypothetical and for illustrative purposes only. Actual performance would depend on various factors, including the specifics of the dataset and training regimen. |
