---
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: []
---

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

# Mistral-7B-v0.1_Emotion

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.

## Model description

https://ai.plainenglish.io/fine-tuning-the-mistral-7b-instruct-v0-1-model-with-the-emotion-dataset-c84c50b553dc

Fine tunning: https://github.com/frank-morales2020/MLxDL/blob/main/FineTuning_Mistral_7b_hfdeployment_dataset_Emotion.ipynb

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

## Intended uses & limitations

More information needed

## Training and evaluation data

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

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

NOTE: test - Accuracy (Eval dataset and predict) for a sample of 2000: 59.45%

-----------

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: 25

NOTE: test - Accuracy (Eval dataset and predict) for a sample of 2000: 79.95%

-----------

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: 100

NOTE: test - Accuracy (Eval dataset and predict) for a sample of 2000: TBD

-----------

## 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: 25

### 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