# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("daspartho/prompt-extend")
model = AutoModelForCausalLM.from_pretrained("daspartho/prompt-extend", device_map="auto")Quick Links
Prompt Extend
Text generation model for generating suitable style cues given the main idea for a prompt.
It is a GPT-2 model trained on dataset of stable diffusion prompts.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.7436 | 1.0 | 12796 | 2.5429 |
| 2.3292 | 2.0 | 25592 | 2.0711 |
| 1.9439 | 3.0 | 38388 | 1.8447 |
| 1.7059 | 4.0 | 51184 | 1.7325 |
| 1.5775 | 5.0 | 63980 | 1.7110 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="daspartho/prompt-extend")