Instructions to use Narsil/small_conversational_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/small_conversational_test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Narsil/small_conversational_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Narsil/small_conversational_test: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/Narsil/small_conversational_test/resolve/main/README.md
- Command line
-
hf download hf://Narsil/small_conversational_test/README.md
-
curl -L -o README.md https://huggingface.co/Narsil/small_conversational_test/resolve/main/README.md
1.35 kB
| ```python | |
| import tempfile | |
| from tokenizers import Tokenizer, models, processors | |
| from transformers.tokenization_utils_fast import PreTrainedTokenizerFast | |
| vocab = [(chr(i), i) for i in range(256)] | |
| tokenizer = Tokenizer(models.Unigram(vocab)) | |
| tokenizer.add_special_tokens(["<bos>", "<eos>"]) | |
| tokenizer.post_processor = processors.TemplateProcessing( | |
| single="<bos> $0 <eos>", special_tokens=[("<bos>", 256), ("<eos>", 257)] | |
| ) | |
| with tempfile.NamedTemporaryFile() as f: | |
| tokenizer.save(f.name) | |
| real_tokenizer = PreTrainedTokenizerFast(tokenizer_file=f.name, eos_token="<eos>", bos_token="<bos>") | |
| real_tokenizer._tokenizer.save("dummy.json") | |
| ``` | |
| Small change. | |