readme: ---
  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: "<s>Prompt: young, curly haired, redhead Natalie Portman as a"
  - text: "<s>Prompt: a powerful energy woman, by alexander fedosav"
  
  inference:
    parameters:
      eos_token_id: 2
      max_length: 128
      
    
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # bloom-560m-finetuned-sd-prompts
  
  This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8742
  
  
  ## Example of usage
  
  ```py
  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
  