Instructions to use kucukkanat/LFM2.5-Encoder-350M-Prompt-Router-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use kucukkanat/LFM2.5-Encoder-350M-Prompt-Router-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('zero-shot-classification', 'kucukkanat/LFM2.5-Encoder-350M-Prompt-Router-ONNX');
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README.md
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@@ -43,6 +43,10 @@ All credit for the model itself goes to [Liquid AI](https://huggingface.co/Liqui
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contains only a re-export; the weights are unchanged apart from quantization, and the original
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[LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router/blob/main/LICENSE) applies.
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Tooling, demo and the export pipeline: <https://github.com/kucukkanat/lfm-encoders>
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## Files
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contains only a re-export; the weights are unchanged apart from quantization, and the original
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[LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router/blob/main/LICENSE) applies.
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**[Try it in your browser →](https://kucukkanat.github.io/lfm-encoders/)** — no install, no API key.
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Tooling, demo and the export pipeline: <https://github.com/kucukkanat/lfm-encoders>
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## Files
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