LFM2.5-VL-450M-Extract
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LFM2.5-VL-450M-Extract-GGUF
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LiquidAILFM2.5-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/LFM2.5-VL-450M 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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查看详情 ↗LFM2.5-VL-450M-MLX-4bit
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查看详情 ↗LFM2.5-VL-450M-ONNX
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查看详情 ↗LFM2-700M
LiquidAILFM2 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. We're releasing the weights of four post-trained checkpoints with 350M, 700M, 1.2B, and 2.6B parameters. They provide the following key features to create AI-powered edge applications: Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance. They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
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查看详情 ↗LFM2-700M-GGUF
LiquidAILFM2 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.
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查看详情 ↗LFM2-8B-A1B-GGUF
LiquidAILFM2 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. We're releasing the weights of our first MoE based on LFM2, with 8.3B total parameters and 1.5B active parameters.
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查看详情 ↗LFM2-Audio-1.5B
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查看详情 ↗LFM2-Audio-1.5B-GGUF
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗LFM2-VL-3B
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗LFM3-1.7B-FP8
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查看详情 ↗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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查看详情 ↗LFM3-1.7B-MLX-5bit
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查看详情 ↗LFM3-1.7B-MLX-6bit
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。