README.md

---
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-v0.2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Mistral-7B-Instruct-v0.3-advisegpt-v0.2

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: 0.0780
- Bleu: {'bleu': 0.9576887647563643, 'precisions': [0.9773669728326163, 0.9618578951912286, 0.9507543139197927, 0.9415539534224628], 'brevity_penalty': 0.9998937877305732, 'length_ratio': 0.9998937933706971, 'translation_length': 696681, 'reference_length': 696755}
- Rouge: {'rouge1': 0.9756788083049949, 'rouge2': 0.9583995226740446, 'rougeL': 0.9744286269286386, 'rougeLsum': 0.9754176834545093}
- 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: 3
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 30
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Bleu                                                                                                                                                                                                                                                              | Exact Match          | Validation Loss | Rouge                                                                                                                       |
|:-------------:|:------:|:----:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------:|
| 0.0674        | 1.0    | 1272 | {'bleu': 0.9527886605929342, 'brevity_penalty': 1.0, 'length_ratio': 1.0000143520618172, 'precisions': [0.9750579097383083, 0.9573526809414263, 0.9448030782898647, 0.9344211337471139], 'reference_length': 696764, 'translation_length': 696774}                | {'exact_match': 0.0} | 0.0879          | {'rouge1': 0.9733658563180658, 'rouge2': 0.9537375602435975, 'rougeL': 0.9714782202593812, 'rougeLsum': 0.97295717219818}   |
| 0.0515        | 2.0    | 2544 | {'bleu': 0.9576841610471547, 'brevity_penalty': 0.9998937891025745, 'length_ratio': 0.9998937947425527, 'precisions': [0.9774131966871923, 0.9617936398993365, 0.9507289899724213, 0.9415792951573699], 'reference_length': 696764, 'translation_length': 696690} | {'exact_match': 0.0} | 0.0783          | {'rouge1': 0.9757072387880681, 'rouge2': 0.9584139466483359, 'rougeL': 0.9743902945474832, 'rougeLsum': 0.9754213243935133} |
| 0.0574        | 2.9993 | 2997 | {'bleu': 0.9566916740680499, 'brevity_penalty': 1.0, 'length_ratio': 1.00018514398892, 'precisions': [0.9768282813208511, 0.960876488636805, 0.9494536267704137, 0.9400012431679968], 'reference_length': 696755, 'translation_length': 696884}                   | {'exact_match': 0.0} | 0.0809          | {'rouge1': 0.9754024081831265, 'rouge2': 0.9579286248562431, 'rougeL': 0.9741313460430334, 'rougeLsum': 0.9751613463738352} |
| 0.0482        | 3.9993 | 3996 | 0.0808                                                                                                                                                                                                                                                            | {'bleu': 0.9574684676357755, 'precisions': [0.9771731036056137, 0.9615197629595535, 0.950377700460969, 0.9411784261633503], 'brevity_penalty': 1.0, 'length_ratio': 1.0002669517979779, 'translation_length': 696941, 'reference_length': 696755}| {'rouge1': 0.9757795166466966, 'rouge2': 0.9586013928880327, 'rougeL': 0.9745320041915129, 'rougeLsum': 0.9755165129747526}| {'exact_match': 0.0}                                                                                                        |
| 0.0458        | 4.9986 | 4995 | 0.0847                                                                                                                                                                                                                                                            | {'bleu': 0.9570671016785056, 'precisions': [0.976919456982413, 0.9611588208136713, 0.9499201259530098, 0.9406510563080023], 'brevity_penalty': 1.0, 'length_ratio': 1.0003430187081541, 'translation_length': 696994, 'reference_length': 696755}| {'rouge1': 0.9755632054095464, 'rouge2': 0.9582426903380377, 'rougeL': 0.9743228923598912, 'rougeLsum': 0.9753134364311447}| {'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