metadata

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

Mistral-7B-v0.1_Emotion

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the generator dataset.

Model description

I developed two notebooks for this article that were thoroughly tested in Google Colab / Google Cloud. noetbook#1 to demonstrate the entire tunning process of the model Mistral-7B-Instruct-v0.1 with the Emotion Dataset dair-ai/emotion, and notebook#2 is for evaluation.

Article: https://medium.com/@frankmorales_91352/fine-tuning-the-mistral-7b-instruct-v0-1-model-with-the-emotion-dataset-c84c50b553dc

Notebook #1: https://github.com/frank-morales2020/MLxDL/blob/main/FineTuning_Mistral_7b_hfdeployment_dataset_Emotion.ipynb

Notebook #2: https://github.com/frank-morales2020/MLxDL/blob/main/FineTunning_Testing_For_EmotionQADataset.ipynb

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: 1

Training results

Framework versions

PEFT 0.11.1
Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1