---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets:
- generator
metrics:
- bleu
- rouge
model-index:
- name: Mistral-7B-Instruct-v0.3-advisegpt-v0.4
  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-Instruct-v0.3-advisegpt-v0.4

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: 0.0776
- Bleu: {'bleu': 0.9592766854579555, 'precisions': [0.9778672968005702, 0.9629777800504739, 0.952562376464522, 0.9440303244645156], 'brevity_penalty': 1.0, 'length_ratio': 1.0002070868729431, 'translation_length': 666525, 'reference_length': 666387}
- Rouge: {'rouge1': 0.9765393241338379, 'rouge2': 0.960274899679536, 'rougeL': 0.9752854409851488, 'rougeLsum': 0.9763366883065228}
- Exact Match: {'exact_match': 0.0}

## Model description

More information needed

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 15
- total_train_batch_size: 15
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Bleu                                                                                                                                                                                                                                              | Rouge                                                                                                                       | Exact Match          |
|:-------------:|:------:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------:|:--------------------:|
| 0.0592        | 0.9998 | 2664 | 0.0792          | {'bleu': 0.957140829496306, 'precisions': [0.9770110285842899, 0.9611535701983837, 0.9499650178830994, 0.9408134298916666], 'brevity_penalty': 1.0, 'length_ratio': 1.0000945396593872, 'translation_length': 666450, 'reference_length': 666387} | {'rouge1': 0.9756420869808171, 'rouge2': 0.958253583847128, 'rougeL': 0.9741670140375769, 'rougeLsum': 0.9753898276329086}  | {'exact_match': 0.0} |
| 0.0518        | 2.0000 | 5329 | 0.0776          | {'bleu': 0.9592766854579555, 'precisions': [0.9778672968005702, 0.9629777800504739, 0.952562376464522, 0.9440303244645156], 'brevity_penalty': 1.0, 'length_ratio': 1.0002070868729431, 'translation_length': 666525, 'reference_length': 666387} | {'rouge1': 0.9765393241338379, 'rouge2': 0.960274899679536, 'rougeL': 0.9752854409851488, 'rougeLsum': 0.9763366883065228}  | {'exact_match': 0.0} |
| 0.0439        | 2.9994 | 7992 | 0.0830          | {'bleu': 0.9593680325138967, 'precisions': [0.97789654044549, 0.9630261327317164, 0.9526617494511856, 0.9442157972615742], 'brevity_penalty': 1.0, 'length_ratio': 1.0001725723941193, 'translation_length': 666502, 'reference_length': 666387}  | {'rouge1': 0.9766709553577743, 'rouge2': 0.9604006931620985, 'rougeL': 0.9753845279467352, 'rougeLsum': 0.9764641972952484} | {'exact_match': 0.0} |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.0
- Datasets 2.19.1
- Tokenizers 0.19.1