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Sponsored by
Vmake Labs Video Enhancer
LiquidAI / LFM2.5-Encoder-350M-Prompt-Router
Model's Last Updated: July 28 2026
text-classification
Introduction of LFM2.5-Encoder-350M-Prompt-Router
Model Details of LFM2.5-Encoder-350M-Prompt-Router
LFM2.5-Encoder-350-Prompt-Router
A full fine-tune of LFM2.5-Encoder-350M with a zero-shot routing head that scores a prompt against user-defined routing lanes in a single encoder pass.
Find more details about our encoders in our blog post .
💻 Demos : Try this fine-tuned model running in a CPU-only Hugging Face space: Zero-shot prompt routing — define your own routing lanes as free text. The model scores the whole prompt against every lane in one pass.
Usage
⚠️ Loads custom code via
trust_remote_code=True(the model wraps atrust_remote_codeencoder).
Install the required packages:
pip install torch transformers
Run zero-shot prompt routing:
from transformers import AutoModel, AutoTokenizer
model_id = "LiquidAI/LFM2.5-Encoder-350-Prompt-Router"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).eval()
routes = ["Coding", "Sales", "Creative writing", "General knowledge"]
prompt = "Can you help me debug a failing Python unit test?"
print(model.route(prompt, routes, tokenizer=tokenizer))
📬 Contact
- Got questions or want to connect? Join our Discord community
- If you are interested in custom solutions with edge deployment, please contact our sales team .
Citation
@article{liquidAI2026Encoders,
author = {Liquid AI},
title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
journal = {Liquid AI Blog},
year = {2026},
note = {www.liquid.ai/blog/lfm2-5-encoders},
}
Runs of LiquidAI LFM2.5-Encoder-350M-Prompt-Router on huggingface.co
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More Information About LFM2.5-Encoder-350M-Prompt-Router huggingface.co Model
Url of LFM2.5-Encoder-350M-Prompt-Router
LFM2.5-Encoder-350M-Prompt-Router huggingface.co Url
Provider of LFM2.5-Encoder-350M-Prompt-Router huggingface.co
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