---
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-without-flash-attention-2
  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-without-flash-attention-2

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.

with dataset b-mc2/sql-create-context

## Model description

More information needed

### Testing results

import torch

from peft import AutoPeftModelForCausalLM

from transformers import AutoTokenizer, pipeline

peft_model_id = "frankmorales2020/Mistral-7B-text-to-sql-without-flash-attention-2"

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

tokenizer = AutoTokenizer.from_pretrained(peft_model_id)

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

CASE Number 1:

prompt='What was the first album Beyoncé released as a solo artist?'

prompt = f"Instruct: generate a SQL query.\n{prompt}\nOutput:\n" # for dataset b-mc2/sql-create-context

outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.eos_token_id)

print('Question: %s'%prompt)

print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")

Question: Instruct: generate a SQL query.
What was the first album Beyoncé released as a solo artist?
Output:

Generated Answer:
SELECT first_album FROM table_name_82 WHERE solo_artist = "beyoncé"

CASE Number 2:

prompt='What was the first album Beyoncé released as a solo artist?'

prompt = f"Instruct: Answer the following question.\n{prompt}\nOutput:\n" 

outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.eos_token_id)

print('Question: %s'%prompt)

print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")

Question: Instruct: Answer the following question.
What was the first album Beyoncé released as a solo artist?
Output:

Generated Answer:
The first album Beyoncé released as a solo artist was "Dangerously in Love".


CASE Number 3:

prompt='What was the first album Beyoncé released as a solo artist?'

prompt = f"Instruct: generate a SQL query.\n{prompt}\n\n" # for dataset b-mc2/sql-create-context

outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.9, top_k=50, top_p=0.1, eos_token_id=pipe.tokenizer.eos_token_id, pad_token_id=pipe.tokenizer.eos_token_id)

print('Question: %s'%prompt)

print(f"Generated Answer:\n{outputs[0]['generated_text'][len(prompt):].strip()}")

Question: Instruct: generate a SQL query.
What was the first album Beyoncé released as a solo artist?

Generated Answer:
```sql
SELECT first_album FROM table_name_84 WHERE solo_artist = "beyoncé"


## 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



### Framework versions

- PEFT 0.10.0
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2