---
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
model-index:
- name: bloom-560m-finetuned-the-stack-prolog
  results: []

widget:
- text: '% Define un hecho que indica que "hello" es un saludo
saludo("hello").

% Define una regla que indica que "world" es un objeto
objeto("world").

% Define una regla que combina el saludo y el objeto para producir la salida "Hola mundo"
hola_mundo :-'
---

<!-- 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-560m-finetuned-the-stack-prolog

This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2433

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2334        | 0.2   | 200  | 0.9993          |
| 0.9174        | 0.4   | 400  | 0.7460          |
| 0.7892        | 0.6   | 600  | 0.6046          |
| 0.6805        | 0.8   | 800  | 0.4964          |
| 0.5898        | 0.99  | 1000 | 0.4283          |
| 0.411         | 1.19  | 1200 | 0.3721          |
| 0.3705        | 1.39  | 1400 | 0.3182          |
| 0.3516        | 1.59  | 1600 | 0.2795          |
| 0.3298        | 1.79  | 1800 | 0.2528          |
| 0.2721        | 1.99  | 2000 | 0.2433          |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.5.1
- Tokenizers 0.13.0