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
Download tokenizer_config.json from peft-internal-testing/tiny-LlamaForSequenceClassification-3.2: direct link, hf CLI and curl.
- Browser
- Download file 325 Bytes
-
https://huggingface.co/peft-internal-testing/tiny-LlamaForSequenceClassification-3.2/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://peft-internal-testing/tiny-LlamaForSequenceClassification-3.2/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/peft-internal-testing/tiny-LlamaForSequenceClassification-3.2/resolve/main/tokenizer_config.json
325 Bytes
| { | |
| "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" | |
| } | |