Mistral-7B-text-to-sql

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Mistral-7B-text-to-sql

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the generator dataset. https://huggingface.co/datasets/b-mc2/sql-create-context

b-mc2/sql-create-context

USE CASE

import torch from peft import AutoPeftModelForCausalLM from transformers import AutoTokenizer, pipeline

peft_model_id = "frankmorales2020/Mistral-7B-text-to-sql"

Load Model with PEFT adapter

model = AutoPeftModelForCausalLM.from_pretrained( peft_model_id, device_map="auto", torch_dtype=torch.float16 )

tokenizer = AutoTokenizer.from_pretrained(peft_model_id)

Load into the pipeline

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

DATASET

https://huggingface.co/datasets/b-mc2/sql-create-context

ARTICLE

https://medium.com/@frankmorales_91352/text-to-sql-generation-a-comprehensive-overview-6feb24f69f3c

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 3
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 6
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 3

Training results

When evaluated on 1000 samples from the evaluation dataset, our model achieved an impressive accuracy of 76.30%. However, there's room for improvement. We could enhance the model's performance by exploring techniques like few-shot learning, RAG, and Self-healing to generate the SQL query.

Framework versions

• PEFT 0.9.0
• Transformers 4.38.2
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2