Instructions to use ai2sql/ai2sql-falcon-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ai2sql/ai2sql-falcon-7b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ybelkada/falcon-7b-sharded-bf16") model = PeftModel.from_pretrained(base_model, "ai2sql/ai2sql-falcon-7b") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Training procedure
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To detail the training procedure for AI2SQL, especially considering its specialized task of converting natural language questions to SQL queries and its basis on the Falcon-7b-instruct model, the following section can be included in the model card:
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### Overview
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AI2SQL was trained in a multi-stage process, starting with a pre-trained Falcon-7b-instruct model, a large transformer-based language model. This base model was then fine-tuned using a Parameter Efficient Fine-Tuning (PEFT) approach with Locally Reweighted Approximations (LoRA) specifically for the task of translating natural language to SQL queries.
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## Training procedure
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### Overview
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AI2SQL was trained in a multi-stage process, starting with a pre-trained Falcon-7b-instruct model, a large transformer-based language model. This base model was then fine-tuned using a Parameter Efficient Fine-Tuning (PEFT) approach with Locally Reweighted Approximations (LoRA) specifically for the task of translating natural language to SQL queries.
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