Text Classification
Transformers
Safetensors
lfm2
feature-extraction
liquid
lfm2.5
bidirectional
masked-lm
encoder
custom_code
Instructions to use suryatmodulus/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 suryatmodulus/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="suryatmodulus/LFM2.5-Encoder-350M-Prompt-Router", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("suryatmodulus/LFM2.5-Encoder-350M-Prompt-Router", trust_remote_code=True) model = AutoModel.from_pretrained("suryatmodulus/LFM2.5-Encoder-350M-Prompt-Router", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from suryatmodulus/LFM2.5-Encoder-350M-Prompt-Router: direct link, hf CLI and curl.
- Browser
- Download file 4.73 MB
-
https://huggingface.co/suryatmodulus/LFM2.5-Encoder-350M-Prompt-Router/resolve/main/tokenizer.json
- Command line
-
hf download hf://suryatmodulus/LFM2.5-Encoder-350M-Prompt-Router/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/suryatmodulus/LFM2.5-Encoder-350M-Prompt-Router/resolve/main/tokenizer.json
4.73 MB
File too large to display, you can check the raw version instead.