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---
base_model: bigscience/bloom-560m
datasets:
- scitldr
library_name: peft
license: bigscience-bloom-rail-1.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Summarization-Bloom-560m
results: []
pipeline_tag: summarization
---
<!-- 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. -->
# Summarization-Bloom-560m
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co./bigscience/bloom-560m) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8578
## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.8487 | 0.2510 | 500 | 2.9019 |
| 2.8069 | 0.5020 | 1000 | 2.8799 |
| 2.8195 | 0.7530 | 1500 | 2.8660 |
| 2.8024 | 1.0040 | 2000 | 2.8556 |
| 2.661 | 1.2550 | 2500 | 2.8637 |
| 2.6136 | 1.5060 | 3000 | 2.8608 |
| 2.5816 | 1.7570 | 3500 | 2.8578 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |