Instructions to use peft-internal-testing/tiny-LlamaForSequenceClassification-3.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peft-internal-testing/tiny-LlamaForSequenceClassification-3.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peft-internal-testing/tiny-LlamaForSequenceClassification-3.2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("peft-internal-testing/tiny-LlamaForSequenceClassification-3.2") model = AutoModelForSequenceClassification.from_pretrained("peft-internal-testing/tiny-LlamaForSequenceClassification-3.2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 325 Bytes
06af1d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|eot_id|>",
"is_local": false,
"local_files_only": false,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"tokenizer_class": "TokenizersBackend"
}
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