- **LFM2.5-Embedding-350M** — A dense bi-encoder, one vector per document. Smallest, fastest index. - **LFM2.5-ColBERT-350M** — A late-interaction model. One vector per *token*, matched via MaxSim. Higher accuracy and better generalization at the cost of index size. Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5-350M-Base. They can be used as a **drop-in replacement** for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross-lingual search across 11 languages. Find more details about the bidirectional architecture and training recipe in our blog post.
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- **LFM2.5-Embedding-350M** — A dense bi-encoder, one vector per document. Smallest, fastest index. - **LFM2.5-ColBERT-350M** — A late-interaction model. One vector per *token*, matched via MaxSim. Higher accuracy and better generalization at the cost of index size. Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5-350M-Base. They can be used as a **drop-in replacement** for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross-lingual search across 11 languages. Find more details about the bidirectional architecture and training recipe in our blog post.
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LFM2.5-Embedding-350M is a dense bi-encoder for fast multilingual retrieval. It produces a single vector per document — the smallest, fastest index — for reliable cross-lingual search across 11 languages. Make requests to embed queries and documents, and rank by cosine similarity (note the asymmetric `query: ` / `document: ` prompt prefixes)
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**LFM2.5-Encoder** is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: Both are masked language models with full bidirectional attention, designed to be fine-tuned into task-specific models (classification, token classification, retrieval, reranking, and semantic similarity) across 15 languages, and to run efficiently on-device. > [!NOTE] > 💻 **Demos**: We built the demos below from fine-tuned LFM2.5-Encoders. Each one runs 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. > - **Zero-shot policy linting** — check text against your company's rules, written as free text. It scores every token against every rule in one pass. > - **Spell checking** — correct misspellings token by token. > - **PII detection** — spot and remove 40 kinds of personal information across 16 languages. > - **Masked-diffusion text generation** — bonus: run the encoder as a chatbot that generates text by iteratively unmasking instead of left to right.
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**LFM2.5-Encoder** is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: Both are masked language models with full bidirectional attention, designed to be fine-tuned into task-specific models (classification, token classification, retrieval, reranking, and semantic similarity) across 15 languages, and to run efficiently on-device. > [!NOTE] > 💻 **Demos**: We built the demos below from fine-tuned LFM2.5-Encoders. Each one runs 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. > - **Zero-shot policy linting** — check text against your company's rules, written as free text. It scores every token against every rule in one pass. > - **Spell checking** — correct misspellings token by token. > - **PII detection** — spot and remove 40 kinds of personal information across 16 languages. > - **Masked-diffusion text generation** — bonus: run the encoder as a chatbot that generates text by iteratively unmasking instead of left to right.
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Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B Then you can type `/image ` to add an image to the chat, then ask `What is in the image?` or any other question about the image. - 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.
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LFM2.5-VL-3B is a multimodal variant of LFM2.5, a family of hybrid models designed for **on-device deployment**. It builds on LFM2-VL-3B with further mid- and post-training. LFM2.5-VL-3B can process both text and images, and uses the LFM2.5-2.6B language model as its backbone, combined with a SigLIP2 NaFlex vision encoder. * **Better grounding**: Improved grounding and object detection with natural language queries. * **Better OCR**: Full page OCR with layout annotation. See layout annotation format for more information. * **Efficient inference**: 228 tok/s on an Apple M5 Max and 116 tok/s on an AMD Ryzen AI Max+ 395, in under 3.3 GB of memory. Find more information about LFM2.5-VL-3B in our release post.
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LFM2.5 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-VL-3B Then you can type `/image ` to add an image to the chat, then ask `What is in the image?` or any other question about the image.
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LFM2.5-VL-3B is a vision-language model built on the LFM2.5-2.6B backbone with a SigLIP2 NaFlex vision encoder (400M). It supports OCR, document comprehension, multilingual vision understanding, bounding box prediction, and function calling.
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LFM2.5‑VL-450M is Liquid AI's refreshed version of the first vision-language model, LFM2-VL-450M, built on an updated backbone LFM2.5-350M and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our blog post. * **Enhanced instruction following** on vision and language tasks. * **Improved multilingual vision understanding** in Arabic, Chinese, French, German, Japanese, Korean, Portuguese and Spanish. * **Bounding box prediction and object detection** for grounded visual understanding. * **Function calling support** for text-only input. 🎥⚡️ You can try LFM2.5-VL-450M running locally in your browser with our real-time video stream captioning WebGPU demo 🎥⚡️
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