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Liquid AI released LFM3-Audio-2B, a compact 2B-parameter audio model that runs locally on laptops and edge devices, supporting voice chat, multilingual speech across 18 languages, audio understanding, and tool calling with an in-house audio encoder and LiquidCodec tokenizer.
Liquid AI released LFM3 on Hugging Face, a ~2B shared-backbone model family spanning text, vision, audio, omni, and decision models, capable of real-time local video captioning in the browser via WebGPU with Transformers.js.
LFM3-VL-2B 视觉语言模型可在浏览器中通过 WebGPU 本地实时进行视频字幕生成，LiquidAI 还发布了对应的 Hugging Face 模型集合与在线演示。
Liquid AI 推出 LFM3 开放权重模型系列，采用卷积、滑窗注意力与全局注意力混合的三层架构，1.7B 文本模型性能超越前代 2.6B 模型，并在 agent 基准上表现突出。
Liquid AI 发布第三代 Liquid Foundation Models（LFM3），首批包含六个 2B 级模型，覆盖文本、视觉、音频、全模态和决策任务，采用开放权重并共享主干架构，可在手机、笔记本和边缘设备上运行，权重已上线 Hugging Face。
A LocalLLaMA user reports negative hands-on experiences with the quantized LFM2.5-24B-A2B (Q4_K_M GGUF) model, citing looping behavior, broken code generation on agentic tasks, and poor plugin development results despite using a corrected chat template and Hermes integration.
Liquid AI 发布 d1-ultra，一款多模态决策模型，在 112 项评测中超越 Jev 1.14 及所有其他决策模型，输出经校准的概率答案，现已在 Liquid API 上线，价格为每百万输入 token 0.042 美元。
Liquid AI released d1-ultra, a multimodal decision model that processes text and images in a single forward pass to return calibrated probabilities without token generation, claiming top performance on the Artificial Analysis Decision Index and pricing at $0.042 per million input tokens.
一位用户分享了对新发布的 LiquidAI LFM2.5-24B-A2B 模型的实测体验，称其在 llama.cpp + ROCm 上可达约 95 tps，Q8 量化占 30GB 显存并支持 128k 上下文，在 Pi Agent 工作流中表现稳定可靠。
Liquid AI 发布了 d1 决策模型家族的两款开放权重模型 d1-3B 和 d1-omni-600M，支持文本、视觉和音频模态，通过单次前向传递直接输出答案，在 Decision Index 上超越同规模模型并可在 NVIDIA Jetson 边缘设备上实现实时推理。
Liquid AI announces a new LFM (Liquid Foundation Model) release happening today, asking the community which model size they'd like to see.
Liquid AI announced that its decision model d1 now supports images, text, or both as inputs, claiming to match or beat GPT-6.1 Sol on 4 of 6 real-world applications while costing 19x to 200x less and responding significantly faster. d1 returns yes/no, choice, or score probabilities in a single forward pass without generating tokens, answering in 200–300 ms.
Liquid AI announced d1 with vision, making its first decision model capable of processing images, text, or both, with benchmarks showing it matches or beats frontier models like GPT-6.1 Sol and Claude Opo 5.5 on real-world applications while staying fast and cheap.
LiquidAI released d1-3B, an open 3B-parameter multimodal decision model built on LFM2.5-VL-3B that answers typed questions over text/JSON/image states in a single forward pass with zero output tokens, achieving 8ms decisions on consumer GPUs.
Today, we release an experimental DSpark draft model for our vision-language model (VLM) LFM2.5-VL-3B, which adds a speculative decoding path for faster inference with minimal memory cost.
The article explains how AI models can get stuck in 'doom loops' and introduces 'antidoom' training using Final Token Preference Optimization (FTPO) to reduce this issue, with a tutorial provided on GitHub.
Liquid AI has released Liquid Nanos, a family of small foundation models (350M–2.6B parameters) that deliver frontier-grade performance on specialized tasks while running on everyday devices.
Liquid AI has released Pipette, an open-source model evaluation suite for on-device AI, developed in partnership with ArtificialAnlys.
Liquid AI introduces Pipette, an on-device model evaluation suite created with ArtificialAnlys to improve benchmarking for edge intelligence.
Liquid AI is anticipated to release a new 100B parameter Liquid Foundation Model soon, leveraging their fast LLM architectures and high-utility SLMs.