LFM2-Audio-1.5B
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LFM2-Audio-1.5B-GGUF
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View details ↗LFM2-ColBERT-350M
LiquidAILFM2-ColBERT-350M is a late interaction retriever with excellent multilingual performance. It allows you to store documents in one language (for example, a product description in English) and retrieve them in many languages with high accuracy. Late interaction retrievers like LFM2-ColBERT-350M are particularly interesting because they preserve much of the **expressivity** of re-rankers while retaining the **efficiency** of bi-encoders. In practice, they're used to both retrieve documents at scale (like bi-encoders) and rank them at the same time (like rerankers). This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
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View details ↗LFM2-ColBERT-350M-GGUF
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View details ↗LFM2-VL-1.6B
LiquidAILFM2‑VL is Liquid AI's first series of multimodal models, designed to process text and images with variable resolutions. Built on the LFM2 backbone, it is optimized for low-latency and edge AI applications. We're releasing the weights of two post-trained checkpoints with 450M (for highly constrained devices) and 1.6B (more capable yet still lightweight) parameters. Find more about our vision-language model in the LFM2-VL post and its language backbone in the LFM2 blog post.
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View details ↗LFM2-VL-1.6B-GGUF
LiquidAILFM2-VL is a new generation of vision 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-VL-1.6B
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View details ↗LFM2-VL-3B
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View details ↗LFM2-VL-3B-GGUF
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View details ↗LFM2-VL-450M
LiquidAILFM2‑VL is Liquid AI's first series of multimodal models, designed to process text and images with variable resolutions. Built on the LFM2 backbone, it is optimized for low-latency and edge AI applications. We're releasing the weights of two post-trained checkpoints with 450M (for highly constrained devices) and 1.6B (more capable yet still lightweight) parameters. * **2× faster inference speed** on GPUs compared to existing VLMs while maintaining competitive accuracy * **Flexible architecture** with user-tunable speed-quality tradeoffs at inference time * **Native resolution processing** up to 512×512 with intelligent patch-based handling for larger images, avoiding upscaling and distortion
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View details ↗LFM2-VL-450M-GGUF
LiquidAILFM2-VL is a new generation of vision 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.
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View details ↗LFM3-1.7B
LiquidAILFM3-1.7B is a **text model for local agents and specialized applications**, based on our new tri-hybrid architecture. Languages English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish LFM3-1.7B is designed for local agents, tool calling, structured outputs, and domain-specific fine-tuning. Use retrieval or search for knowledge-heavy tasks. It is not recommended for agentic coding or complex multi-step reasoning due to its small size.
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View details ↗LFM3-1.7B-FP8
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View details ↗LFM3-1.7B-GGUF
LiquidAIGGUF version of LFM3-1.7B for local inference on CPUs and GPUs. Find architecture and usage details in the original model card. Recommended sampling: temperature 0.1, top_k 50, repeat penalty 1.1. For math and science: temperature 1.0, top_p 0.95, top_k 20.
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View details ↗LFM3-1.7B-MLX-5bit
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View details ↗LFM3-1.7B-MLX-6bit
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View details ↗LFM3-1.7B-MLX-8bit
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View details ↗LFM3-1.7B-MLX-bf16
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View details ↗LFM3-1.7B-NVFP4
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View details ↗LFM3-1.7B-ONNX
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View details ↗LFM3-Audio-2B
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View details ↗LFM3-Audio-2B-GGUF
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View details ↗LFM3-Audio-2B-MLX-bf16
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View details ↗LFM3-Omni-2B
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View details ↗LFM3-Omni-2B-GGUF
LiquidAILFM3-Omni-2B is an omni-modal model that understands text, images, video and audio, and answers in text and speech. It is built for on-device use. Recommended sampling: greedy decoding (temperature 0) for text output. For speech output, audio temperature 0.4 and audio repetition penalty 1.2. > [!NOTE] > Supported in llama.cpp b11960 (v0.8.0) and newer, including real-time voice conversations (with tool calls) on the `/v1/realtime` WebSocket API of `llama serve`.
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