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