115 lines
3.3 KiB
Markdown
115 lines
3.3 KiB
Markdown
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---
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license: bigscience-bloom-rail-1.0
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tags:
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- generated_from_trainer
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- stable-diffusion
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- diffusion
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model-index:
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- name: bloom-560m-finetuned-sd-prompts
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results: []
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datasets:
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- Gustavosta/Stable-Diffusion-Prompts
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widget:
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- text: "<s>Prompt: young, curly haired, redhead Natalie Portman as a"
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- text: "<s>Prompt: a powerful energy woman, by alexander fedosav"
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inference:
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parameters:
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eos_token_id: 2
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max_length: 128
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bloom-560m-finetuned-sd-prompts
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.8742
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## Example of usage
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```py
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import torch
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from transformers import BloomTokenizerFast, BloomForCausalLM
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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ckpt = 'mrm8488/bloom-560m-finetuned-sd-prompts'
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tokenizer = BloomTokenizerFast.from_pretrained(ckpt)
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model = BloomForCausalLM.from_pretrained(ckpt).to(device)
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def generate_prompt(text):
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inputs = tokenizer(text, return_tensors='pt')
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input_ids = inputs.input_ids.to(device)
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attention_mask = inputs.attention_mask.to(device)
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output = model.generate(input_ids, attention_mask=attention_mask, repetition_penalty=1.05, max_length=2048, eos_token_id=tokenizer.eos_token_id)
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return tokenizer.decode(output[0], skip_special_tokens=False)
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text = "<s>Prompt: pikachu dinning in the eiffel tower"
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generate_prompt(text)
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# 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>
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```
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 2
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 2.6743 | 0.17 | 100 | 2.0891 |
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| 1.8919 | 0.33 | 200 | 1.7191 |
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| 1.5907 | 0.5 | 300 | 1.4454 |
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| 1.3865 | 0.67 | 400 | 1.3247 |
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| 1.2487 | 0.83 | 500 | 1.2150 |
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| 1.1565 | 1.0 | 600 | 1.1031 |
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| 0.896 | 1.17 | 700 | 1.0612 |
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| 0.8389 | 1.33 | 800 | 0.9994 |
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| 0.8071 | 1.5 | 900 | 0.9530 |
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| 0.7628 | 1.67 | 1000 | 0.9206 |
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| 0.7423 | 1.83 | 1100 | 0.8883 |
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| 0.7155 | 2.0 | 1200 | 0.8742 |
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### Framework versions
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- Transformers 4.22.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.5.1
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- Tokenizers 0.12.1
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