RichardErkhov/mrm8488_-_bloom-560m-finetuned-sd-prompts-4bits

Quantization made by Richard Erkhov.

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bloom-560m-finetuned-sd-prompts - bnb 4bits

• Model creator: https://huggingface.co/mrm8488/
• Original model: https://huggingface.co/mrm8488/bloom-560m-finetuned-sd-prompts/

Original model description:

license: bigscience-bloom-rail-1.0 tags:

• generated_from_trainer
• stable-diffusion
• diffusion model-index:
• name: bloom-560m-finetuned-sd-prompts results: []

datasets:

• Gustavosta/Stable-Diffusion-Prompts

widget:

• text: "Prompt: young, curly haired, redhead Natalie Portman as a"
• text: "Prompt: a powerful energy woman, by alexander fedosav"

inference: parameters: eos_token_id: 2 max_length: 128

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