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

Framework versions

• Transformers 4.22.1
• Pytorch 1.12.1+cu113
• Datasets 2.5.1
• Tokenizers 0.12.1