---
base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets:
- generator
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-text-to-sql-flash-attention-2-dataeval
  results: []
---

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# Mistral-7B-text-to-sql-flash-attention-2-dataeval

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the generator dataset.
It achieves the following results on the evaluation set:

- Loss: 0.4605

Perplexity of 10.40

Perplexity: Perplexity is a measure of how uncertain or surprised the model is about its predictions. 
It's derived from the probabilities the model assigns to different words or tokens.

Perplexity Article: https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf 
https://medium.com/@AyushmanPranav/perplexity-calculation-in-nlp-0699fbda4594

 The perplexity of 10.40 achieved on the dataset indicates that the fine-tuned Mistral-7B model reasonably understands natural language and SQL syntax. 
 However, further evaluation using task-specific metrics is necessary to assess the model's effectiveness in real-world scenarios. 
 By combining quantitative metrics like perplexity with qualitative analysis of generated queries, 
 we can comprehensively understand the model's strengths and weaknesses, ultimately 
 leading to improved performance and more reliable text-to-SQL translation capabilities. 
 

Dataset : [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)

## Model description

Article: https://medium.com/@frankmorales_91352/fine-tuning-the-llm-mistral-7b-instruct-v0-3-249c1814ceaf

## Training and evaluation data

Fine Tuning and Evaluation: https://github.com/frank-morales2020/MLxDL/blob/main/FineTuning_LLM_Mistral_7B_Instruct_v0_1_for_text_to_SQL_EVALDATA.ipynb

Evaluation: https://github.com/frank-morales2020/MLxDL/blob/main/Evaluator_Mistral_7B_text_to_sql.ipynb

Evaluation article with Chromadb: https://medium.com/@frankmorales_91352/a-comprehensive-evaluation-of-a-fine-tuned-text-to-sql-model-from-code-to-results-with-7ea59943b0a1

Evaluation article with Chromadb, PostgreSQL and the “gretelai/synthetic_text_to_sql” dataset: 
https://medium.com/@frankmorales_91352/evaluating-the-performance-of-a-fine-tuned-text-to-sql-model-6b7d61dcfef5
The article discusses evaluating this fine-tuned text-to-SQL model, a type of artificial intelligence 
that translates natural language into SQL queries. 

The model was trained on the "b-mc2/sql-create-context" dataset and 
evaluated using the "gretelai/synthetic_text_to_sql" dataset.

### 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: 8
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- lr_scheduler_warmup_steps: 15
- num_epochs: 3

from transformers import TrainingArguments

args = TrainingArguments(
    output_dir="Mistral-7B-text-to-sql-flash-attention-2-dataeval", 
    
    num_train_epochs=3,                     # number of training epochs
    per_device_train_batch_size=3,          # batch size per device during training
    gradient_accumulation_steps=8,      #2  # number of steps before performing a backward/update pass
    gradient_checkpointing=True,            # use gradient checkpointing to save memory
    optim="adamw_torch_fused",              # use fused adamw optimizer
    logging_steps=10,                       # log every ten steps
    #save_strategy="epoch",                  # save checkpoint every epoch
    learning_rate=2e-4,                     # learning rate, based on QLoRA paper
    bf16=True,                              # use bfloat16 precision
    tf32=True,                              # use tf32 precision
    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper
    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper
    weight_decay=0.01,
    lr_scheduler_type="constant",           # use constant learning rate scheduler
    push_to_hub=True,                       # push model to hub
    report_to="tensorboard",                # report metrics to tensorboard
    hub_token=access_token_write,           # Add this line
    load_best_model_at_end=True,
    logging_dir="/content/drive/MyDrive/model/Mistral-7B-text-to-sql-flash-attention-2-dataeval/logs",
    evaluation_strategy="steps",
    eval_steps=10,
    save_strategy="steps",
    save_steps=10,
    metric_for_best_model = "loss",
    warmup_steps=15,
    
)

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.8612        | 0.4020 | 10   | 0.6092          |
| 0.5849        | 0.8040 | 20   | 0.5307          |
| 0.4937        | 1.2060 | 30   | 0.4887          |
| 0.4454        | 1.6080 | 40   | 0.4670          |
| 0.425         | 2.0101 | 50   | 0.4544          |
| 0.3498        | 2.4121 | 60   | 0.4717          |
| 0.3439        | 2.8141 | 70   | 0.4605          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1