---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- generator
library_name: peft
license: llama3.1
metrics:
- accuracy
- bleu
- rouge
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama3.1-8b-instruct-SFT-2024-09-18_LoRAs
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Llama3.1-8b-instruct-SFT-2024-09-18_LoRAs

This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9931
- Accuracy: 0.0009
- Bleu: 0.5303
- Rouge1: 0.7979
- Rouge2: 0.5322
- Rougel: 0.6881
- Rougelsum: 0.7836
- Perplexity: 2.6995

## 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: 1e-05
- train_batch_size: 6
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1.5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Bleu   | Rouge1 | Rouge2 | Rougel | Rougelsum | Perplexity |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:---------:|:----------:|
| 1.8865        | 0.0812 | 100  | 1.4597          | 0.0010   | 0.4381 | 0.7484 | 0.4472 | 0.6115 | 0.7309    | 4.3047     |
| 1.556         | 0.1625 | 200  | 1.3056          | 0.0007   | 0.4613 | 0.7654 | 0.4686 | 0.6322 | 0.7469    | 3.6898     |
| 1.4258        | 0.2437 | 300  | 1.2155          | 0.0007   | 0.4815 | 0.7745 | 0.4848 | 0.6475 | 0.7567    | 3.3720     |
| 1.3455        | 0.3249 | 400  | 1.1721          | 0.0007   | 0.4877 | 0.7756 | 0.4906 | 0.6551 | 0.7588    | 3.2287     |
| 1.2937        | 0.4062 | 500  | 1.1408          | 0.0009   | 0.4944 | 0.7808 | 0.4958 | 0.6591 | 0.7640    | 3.1291     |
| 1.2661        | 0.4874 | 600  | 1.1149          | 0.0008   | 0.5032 | 0.7854 | 0.5060 | 0.6659 | 0.7694    | 3.0491     |
| 1.2571        | 0.5686 | 700  | 1.0901          | 0.0009   | 0.5081 | 0.7897 | 0.5105 | 0.6707 | 0.7749    | 2.9745     |
| 1.2529        | 0.6499 | 800  | 1.0758          | 0.0009   | 0.5130 | 0.7888 | 0.5151 | 0.6739 | 0.7720    | 2.9322     |
| 1.2122        | 0.7311 | 900  | 1.0639          | 0.0010   | 0.5133 | 0.7895 | 0.5158 | 0.6737 | 0.7737    | 2.8974     |
| 1.2081        | 0.8123 | 1000 | 1.0521          | 0.0009   | 0.5144 | 0.7902 | 0.5148 | 0.6755 | 0.7743    | 2.8636     |
| 1.1804        | 0.8936 | 1100 | 1.0411          | 0.0009   | 0.5175 | 0.7920 | 0.5192 | 0.6789 | 0.7770    | 2.8321     |
| 1.1616        | 0.9748 | 1200 | 1.0311          | 0.0009   | 0.5209 | 0.7924 | 0.5205 | 0.6794 | 0.7764    | 2.8041     |
| 1.1607        | 1.0561 | 1300 | 1.0244          | 0.0010   | 0.5215 | 0.7935 | 0.5243 | 0.6812 | 0.7787    | 2.7855     |
| 1.1554        | 1.1373 | 1400 | 1.0168          | 0.0010   | 0.5241 | 0.7953 | 0.5258 | 0.6830 | 0.7800    | 2.7642     |
| 1.153         | 1.2185 | 1500 | 1.0103          | 0.0009   | 0.5263 | 0.7957 | 0.5263 | 0.6841 | 0.7812    | 2.7464     |
| 1.1488        | 1.2998 | 1600 | 1.0032          | 0.0009   | 0.5250 | 0.7969 | 0.5284 | 0.6842 | 0.7815    | 2.7268     |
| 1.1488        | 1.3810 | 1700 | 0.9979          | 0.0010   | 0.5280 | 0.7979 | 0.5281 | 0.6864 | 0.7819    | 2.7126     |
| 1.1529        | 1.4622 | 1800 | 0.9931          | 0.0009   | 0.5303 | 0.7979 | 0.5322 | 0.6881 | 0.7836    | 2.6995     |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.0.1+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1