Text Classification
Transformers
ONNX
Safetensors
English
llama
reasoning-detection
small-model
tiny-model
english
34m
text-embeddings-inference
Instructions to use vrishv/thinker-34M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vrishv/thinker-34M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vrishv/thinker-34M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vrishv/thinker-34M") model = AutoModelForSequenceClassification.from_pretrained("vrishv/thinker-34M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from vrishv/thinker-34M: direct link, hf CLI and curl.
- Browser
- Download file 273 Bytes
-
https://huggingface.co/vrishv/thinker-34M/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://vrishv/thinker-34M/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/vrishv/thinker-34M/resolve/main/tokenizer_config.json
273 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|endoftext|>", | |
| "eos_token": "<|endoftext|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |