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metadata
license: apache-2.0
library_name: peft
tags:
  - trl
  - sft
  - generated_from_trainer
datasets:
  - generator
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
  - name: Mistral-7B-text-to-sql
    results: []

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