---
base_model: bigscience/bloom-1b1
datasets:
- scitldr
library_name: peft
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
model-index:
- name: Bloom-1b1-Summarization-QLoRa
  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. -->

# Bloom-1b1-Summarization-QLoRa

This model is a fine-tuned version of [bigscience/bloom-1b1](https://huggingface.co/bigscience/bloom-1b1) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7202

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.6959        | 0.2510 | 500  | 2.7513          |
| 2.6632        | 0.5020 | 1000 | 2.7296          |
| 2.6724        | 0.7530 | 1500 | 2.7230          |
| 2.6625        | 1.0040 | 2000 | 2.7177          |
| 2.5181        | 1.2550 | 2500 | 2.7247          |
| 2.4633        | 1.5060 | 3000 | 2.7230          |
| 2.4341        | 1.7570 | 3500 | 2.7202          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1