Instructions to use OpCustom/OpAI1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use OpCustom/OpAI1.1 with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("OpCustom/OpAI1.1", set_active=True) - Notebooks
- Google Colab
- Kaggle
File size: 895 Bytes
70a6fd7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | import torch
class SimpleTokenizer:
def __init__(self, vocab_path):
self.char_to_idx = torch.load(vocab_pth)
# Add <unk> if not in vocab
if '<unk>' not in self.char_to_idx:
self.char_to_idx['<unk>'] = max(self.char_to_idx.values()) + 1
self.idx_to_char = {i: c for c, i in self.char_to_idx.items()}
def encode(self, text):
return [self.char_to_idx.get(c, self.char_to_idx.get('<unk>', 0)) for c in text]
def decode(self, indices):
return ''.join([self.idx_to_char.get(i, '') for i in indices])
# Example usage
vocab_path = 'vocab.pth' # Replace with the actual path to your vocab file
tokenizer = SimpleTokenizer(vocab_path)
text = "Hello, world!"
tokens = tokenizer.encode(text) # Use the encode method here
print(tokens)
decoded_text = tokenizer.decode(tokens)
print(decoded_text)
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