transformers safetensors bloom text-generation generated_from_trainer base_model:bigscience/bloom-1b7 base_model:finetune:bigscience/bloom-1b7 license:bigscience-bloom-rail-1.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: bigscience/bloom-1b7
license: bigscience-bloom-rail-1.0
tags:
- generated_from_trainer
model-index:
- name: Bloom-1b7-creative-writing
  results: []
---

# Bloom-1b7-creative-writing

This model is a fine-tuned version of [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7) on the [adambjorn/UnrelatedForgettingOverhead](https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead) creative writing dataset.

## Model description

More information needed

## Intended uses & limitations

Intended for use on a student group project for Portland State University's Winter 2024 LLMs Course.

## Training and evaluation data

Instruction Tuned on the creative writing dataset here: https://huggingface.co/datasets/adambjorn/UnrelatedForgettingOverhead/viewer/creative

## Training procedure

Trained on a single RTX 3090 card.

Given a set of prompts:

```python
prompts = [
    "Write a creative short story based on the following title:",
    "Here is a title for a story. Craft a short narrative around it:",
    "Using the title given, develop a short story:",
    "Imagine a short story that starts with this title:",
    "Create a brief story with the following title:"
]
```

Concatenate the prompt, the title and the story like so:

```python
concatenated_texts = [random.choice(prompts) + " " + title + "</s>" + "Story: " + selftext for title, selftext in zip(titles, selftexts)]
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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: 5
- mixed_precision_training: Native AMP

### Training results
Final results:
{'loss': 0.0472, 'learning_rate': 1.4893617021276598e-06, 'epoch': 4.95}

Average results:
{'train_runtime': 563.2707,
'train_samples_per_second': 1.687,
'train_steps_per_second': 0.417,
'train_loss': 0.8475136074614018,
'epoch': 4.95}


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
