| --- |
| language: |
| - en |
| - de |
| - es |
| - fr |
| - it |
| - nl |
| - pl |
| - pt |
| - ar |
| - hi |
| - ja |
| - ru |
| - tr |
| - vi |
| - zh |
| tags: |
| - liquid |
| - lfm2 |
| - lfm2.5 |
| - bidirectional |
| - masked-lm |
| - encoder |
| library_name: transformers |
| license: other |
| license_name: lfm1.0 |
| license_link: LICENSE |
| pipeline_tag: text-classification |
| base_model: |
| - LiquidAI/LFM2.5-Encoder-350M |
| --- |
| |
| <div align="center"> |
| <img |
| src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" |
| alt="Liquid AI" |
| style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;" |
| /> |
| <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;"> |
| <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • |
| <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • |
| <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • |
| <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a> |
| </div> |
| </div> |
| |
| # LFM2.5-Encoder-350-Prompt-Router |
|
|
| A full fine-tune of [LFM2.5-Encoder-350M](https://huggingface.co/LiquidAI/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](https://www.liquid.ai/blog/lfm2-5-encoders). |
|
|
| > [!NOTE] |
| > 💻 **Demos**: Try this fine-tuned model running in a CPU-only Hugging Face space: |
| > **[Zero-shot prompt routing](https://huggingface.co/spaces/LiquidAI/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 a `trust_remote_code` encoder). |
|
|
| Install the required packages: |
|
|
| ```bash |
| pip install torch transformers |
| ``` |
|
|
| Run zero-shot prompt routing: |
|
|
| ```python |
| 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](https://discord.com/invite/liquid-ai) |
| - If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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}, |
| } |
| ``` |
|
|
