peft safetensors trl sft generated_from_trainer dataset:dialogstudio base_model:TencentARC/LLaMA-Pro-8B license:llama2 region:us
---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- dialogstudio
base_model: TencentARC/LLaMA-Pro-8B
model-index:
- name: llama-pro-8b-tweet-summarization
  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. -->

# llama-pro-8b-tweet-summarization

This model is a fine-tuned version of [TencentARC/LLaMA-Pro-8B](https://huggingface.co/TencentARC/LLaMA-Pro-8B) on the dialogstudio dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0033
- Rouge Scores: {'rouge1': 74.5645505147687, 'rouge2': 61.793005354430264, 'rougeL': 50.4897941651719, 'rougeLsum': 74.49500409220269}
- Bleu Scores: [0.699414840916846, 0.6885479681674689, 0.6673588982582369, 0.6432031117261758]
- Gen Len: 463.0182

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge Scores                                                                                                             | Bleu Scores                                                                      | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------:|:--------:|
| 1.9079        | 1.0   | 220  | 1.8554          | {'rouge1': 92.82930837423424, 'rouge2': 78.69002098258808, 'rougeL': 67.77525397424012, 'rougeLsum': 92.83122136025193}  | [0.8742055507510261, 0.85321871129453, 0.8273357083346458, 0.7999205981632427]   | 463.0182 |
| 1.6535        | 2.0   | 440  | 1.8644          | {'rouge1': 93.48067160777316, 'rouge2': 78.9876103970411, 'rougeL': 67.83658288925474, 'rougeLsum': 93.48402466797468}   | [0.8755971013914376, 0.8572483593980601, 0.8317815914576417, 0.8041763182250138] | 463.0182 |
| 1.282         | 3.0   | 660  | 2.0002          | {'rouge1': 87.29036568539799, 'rouge2': 73.18485374150632, 'rougeL': 62.10087123916552, 'rougeLsum': 87.26501626335327}  | [0.8760501251112071, 0.8593531308703309, 0.8335667371919002, 0.8051115198870601] | 463.0182 |
| 0.8481        | 4.0   | 880  | 2.2502          | {'rouge1': 86.93286796220396, 'rouge2': 72.7995944273867, 'rougeL': 61.376242795856115, 'rougeLsum': 86.92669954280056}  | [0.8749955177244617, 0.8580816714753104, 0.8319806879994025, 0.8031812686772342] | 463.0182 |
| 0.5026        | 5.0   | 1100 | 2.5319          | {'rouge1': 74.55412702158021, 'rouge2': 61.949690968753835, 'rougeL': 51.12580948921186, 'rougeLsum': 74.48696099641717} | [0.7003890180040002, 0.689128452909103, 0.6682005373110111, 0.6444964084098124]  | 463.0182 |
| 0.297         | 6.0   | 1320 | 2.8374          | {'rouge1': 74.57349965516465, 'rouge2': 61.85762409604638, 'rougeL': 50.76329385869279, 'rougeLsum': 74.51078702126195}  | [0.6993958025399786, 0.6884661028969841, 0.6674353154479407, 0.643507030069284]  | 463.0182 |
| 0.2129        | 7.0   | 1540 | 3.0033          | {'rouge1': 74.5645505147687, 'rouge2': 61.793005354430264, 'rougeL': 50.4897941651719, 'rougeLsum': 74.49500409220269}   | [0.699414840916846, 0.6885479681674689, 0.6673588982582369, 0.6432031117261758]  | 463.0182 |


### Framework versions

- PEFT 0.8.2.dev0
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.1