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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, 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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