---
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: []
---

<!-- 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-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

## 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

### 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 10 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/gdrive/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