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bloom-560m-finetuned-sd-prompts
This model is a fine-tuned version of bigscience/bloom-560m on the Gustavosta/Stable-Diffusion-Prompts dataset. It achieves the following results on the evaluation set:
- Loss: 0.8742
Example of usage
import torch
from transformers import BloomTokenizerFast, BloomForCausalLM
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'mrm8488/bloom-560m-finetuned-sd-prompts'
tokenizer = BloomTokenizerFast.from_pretrained(ckpt)
model = BloomForCausalLM.from_pretrained(ckpt).to(device)
def generate_prompt(text):
inputs = tokenizer(text, return_tensors='pt')
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask, repetition_penalty=1.05, max_length=2048, eos_token_id=tokenizer.eos_token_id)
return tokenizer.decode(output[0], skip_special_tokens=False)
text = "<s>Prompt: pikachu dinning in the eiffel tower"
generate_prompt(text)
# Output: <s>Prompt: pikachu dinning in the eiffel tower, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha</s>
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: 1
- 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 |
|---|---|---|---|
| 2.6743 | 0.17 | 100 | 2.0891 |
| 1.8919 | 0.33 | 200 | 1.7191 |
| 1.5907 | 0.5 | 300 | 1.4454 |
| 1.3865 | 0.67 | 400 | 1.3247 |
| 1.2487 | 0.83 | 500 | 1.2150 |
| 1.1565 | 1.0 | 600 | 1.1031 |
| 0.896 | 1.17 | 700 | 1.0612 |
| 0.8389 | 1.33 | 800 | 0.9994 |
| 0.8071 | 1.5 | 900 | 0.9530 |
| 0.7628 | 1.67 | 1000 | 0.9206 |
| 0.7423 | 1.83 | 1100 | 0.8883 |
| 0.7155 | 2.0 | 1200 | 0.8742 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
