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
Update router model card and remote code
#1
by iamleonie - opened
Adds the drafted model card, LFM Open License file, and remote-code routing class used by the README usage snippet.
fernandofernandes changed pull request status to open
fernandofernandes changed pull request status to merged