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
- Xet hash:
- 9c27359458d6cd9a47f793238e864640a07b95fb90f518a090e16c934eb45738
- Size of remote file:
- 1.42 GB
- SHA256:
- 9fab23eeb312d951bca8a0dfa4068ca4be4d55283a66c6fe5cbe9cf1e14e631d
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