Text Classification
Transformers
Safetensors
lfm2
feature-extraction
liquid
lfm2.5
bidirectional
masked-lm
encoder
custom_code
Instructions to use LiquidAI/LFM2.5-Encoder-350M-Prompt-Router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2.5-Encoder-350M-Prompt-Router with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", trust_remote_code=True) model = AutoModel.from_pretrained("LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 306 Bytes
35ca4a0 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"backend": "tokenizers",
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"is_local": false,
"mask_token": "<|mask|>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<|pad|>",
"tokenizer_class": "TokenizersBackend"
}
|