Bloom-1b7-creative-writing
This model is a fine-tuned version of bigscience/bloom-1b7 on the 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:
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:
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