---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Mistral-7B-text-to-sql

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/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