液态AI拥有发布 LFM3，它的新一代液态基础模型。 Liquid AI LFM3是一个由6个开放量级，2B级模型组成的家族，以一个共享骨干为基础。 家族包括文字，视觉，音频，omni和决定等任务。 每个型号都可以在单个GPU上进行微调。 然后开发者可以部署在手机，笔记本电脑，私人服务器上或云上。
TL; DR 数据
- 大小：LFM3-1.7B有1.73B参数和一个256K上下文窗口。 LFM3-VL-2B有1.92B和128K. LFM3-Omni-2B有2.2B.
- 运行在：大约1GB的内存在4位，所以手机和笔记本电脑。 GGUF,MLX,ONNX,FP8和NVFP4检查站船。
- 性能：LFM3-1.7B在29个基准测试平均49.37分，高于47.24分的Qwen3.5-9B(立基德自己的eval).
- 最佳：IFEval 96.66，和自动化本克 41.29 vs 15.10 Qwen3.5-9B.
- 最糟糕的是：人类的最后考试(没有工具)7.17，而RULAR 128K 65.56对87.11对Qwen3.5-9B.
什么是液态AI LFM3?
LFM3是一个来自液态AI的多模式模式家族，从1个基座分出6个模型。 它们都从LFM3-1.7B-Base开始。
释放内容包括：
- LFM3-1.7B：当地agents的文本模型，赫尔墨斯Agent，皮和OpenClaw等被后期训练的被带。
- LFM3-VL-2B：首个有视频输入的LFM视觉模型，以2英尺为样本。
- LFM3-Audio-2B：使用18种语言进行发言，并有工具呼叫。
- LFM3-Omni-2B：一个模型中的视觉、音频和语言，在文本或语音中回答。
- d1-2B和d1-Omni-2B：在1个前传中解答许多问题的决定模型。
三合会建筑如何运作？.
骨干搭配了3个操作员，跨越了24个层。 共有13个入门短分层，5个滑入窗口的注意力层和6个全球注意力层。 SWA窗口为256个符。 革命内核由3个增长到7个。
Liquid AI团队用它的硬件入室架构搜索发现了这个混音。 LFM2在1个电话和1个笔记本电脑上调音。 LFM3被调用到14个设备和后端组合。 其中包括一款Raspberry Pi 5和Qualcomm Hexagon NPU.
其他变化包括：注意力上的头相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相相基站型号看到了22T个地标。 最后的3.5T令牌是agent-重力中训，其上下文也扩展到了256K.
培训后分四个阶段进行。 在1.75T上SFT先出道后被混合RL. 其次，6名专业教师被分解成1个模式，然后是代理RL。
LFM3-1.7B在基准测试上表现如何？.
液态AI报告LFM3-1.7B以35%的参数比LFM2.5-2.6B低。 更大的故事是agent结果。 在WebArena-Verifed上，它得分为38.42分，而Qwen3.5-9B的得分则为28.94分。 在BFCL V4上，它得分71.39分，对62.83分。
大小仍然在知识和代理编码上取胜。 GPQA Diamond为63.37,Qwen3.5-9B对80.40. SWE-bench验证为43.40，对51.20. 搜索缩小了差距："人类"最后的考试从7.17起，用工具提升到13.90起。
LFM3-1.7B与对手的对比如何？.
资料来源：液态LFM3型卡，MiniCPM5-2B型卡，Qwen3.5-4B型卡，Gemma4 E4B型卡，LFM牌照。
视觉、音频、奥姆尼和决定模型呢？
LFM3-VL-2B在62个视星等中平均为71.4分基准测试。 Gemma 4 12B在同一套房上得分为71.7分。 它导致在视觉地基(77.3)和GUI理解(83.1)上进行比较。 视频(61.2)和代理任务(42.5)仍然会跟踪更大的API模型。
LFM3-Audio-2B达到6.2英格兰WER和3.8 UTMOSv2 TTS分数。 LFM3-Omni-2B在"每日-Omni"上得分70.1分，而Qwen2.5-Omni-7B则得分61.3分。
d1-2B取而代之的是d1-3B，取而代之的是三分之一的参数。 它改进了裁判室，从83.6到85.2。
开发者如何部署 LFM3?
重量在Hugging Face，液态AI游戏场和液态API上。 文件列出了GGUF,MLX和ONNX的文本和视觉模型。 运行时间包括：llama.cpp,MLX,vLLM,SGLang,Ollama和变形器。
端侧，液态AI在Q4 0测量出有32K上下文的llama.cpp. 完整的128K上下文需要1.6GB的KV缓存。 Qwen3.5-4B需要4.3GB. 在1NVIDIA B300上加有SGLang后，LFM3-1.7B在每天出出出3.6B令牌时，以1,024个同时请求。
液态AI也显示出专业化。 1A100上数小时SFT和GRPO将风涡缺陷分类提升至67%.
关键外卖
——6个开口量级2B级型号共享一款三合一骨干。
- LFM3-1.7B以有256K上下文的4-位相适应于约1GB.
- 它在BFCL V4上击败了Qwen3.5-9B,WebArena-Verifed和自动化奔驰。
- 知识和代理编码仍然是明显的弱点。
- 免费商业使用每年收入为1 000万美元。
看看技术细节模型重量和文件并加入我们的通讯。 等等，你在电报里吗？现在你也可以加入我们的电报。
需要和我们合作推广您的 GitHub Repo OR Hugging Face Page OR Product Release OR Webinar等吗？联系我们
Asif Razzaq是Marktechpost AI Media Inc.的首席执行官。 作为一个有远见的企业家和工程师，阿西夫致力于利用人工智能的潜力为社会造福。 他最近的努力是人工智能媒体平台的发布，该平台以深入报道机器学习和深层学习新闻而出名，这些新闻在技术上既可靠又为广大观众所容易理解。 该平台每月有超过200万次观看，表明其受观众欢迎。
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Liquid AI has released LFM3, its new generation of Liquid Foundation Models. Liquid AI LFM3 is a family of 6 open-weight, 2B-class models built on one shared backbone. The family covers text, vision, audio, omni and decision tasks. Every model can be fine-tuned on a single GPU. Developers can then deploy on phones, laptops, private servers or the cloud.
TL;DR
- Size: LFM3-1.7B has 1.73B parameters and a 256K context window. LFM3-VL-2B has 1.92B and 128K. LFM3-Omni-2B has 2.2B.
- Runs on: about 1 GB of memory at 4-bit, so phones and laptops. GGUF, MLX, ONNX, FP8 and NVFP4 checkpoints ship.
- Performance: LFM3-1.7B averages 49.37 across 29 benchmarks, above Qwen3.5-9B at 47.24 (Liquid’s own eval).
- Best: IFEval 96.66, and AutomationBench 41.29 vs 15.10 for Qwen3.5-9B.
- Worst: Humanity’s Last Exam (no tools) 7.17, and RULER 128K 65.56 vs 87.11 for Qwen3.5-9B.
What is Liquid AI LFM3?
LFM3 is a multimodal model family from Liquid AI that branches 6 models from 1 base. All of them start from LFM3-1.7B-Base.
The release includes:
- LFM3-1.7B: a text model for local agents, post-trained for harnesses like Hermes Agent, Pi and OpenClaw.
- LFM3-VL-2B: the first LFM vision model with video input, sampled at 2 fps.
- LFM3-Audio-2B: speech in and out across 18 languages, with tool calling.
- LFM3-Omni-2B: vision, audio and language in one model, answering in text or speech.
- d1-2B and d1-Omni-2B: decision models that answer many questions in 1 forward pass.
How does the tri-hybrid architecture work?
The backbone mixes 3 operators across 24 layers. There are 13 gated short convolutions, 5 sliding-window attention layers and 6 global attention layers. The SWA window is 256 tokens. Convolution kernels grew from 3 to 7.
Liquid AI team found this mix with its hardware-in-the-loop architecture search. LFM2 was tuned on 1 phone and 1 laptop. LFM3 was tuned across 14 device and backend combinations. These include a Raspberry Pi 5 and the Qualcomm Hexagon NPU.
Other changes include head-wise sigmoid gates on attention, partial RoPE on global layers and sandwich norm. The base model saw 22T tokens. The last 3.5T tokens were agent-heavy mid-training, which also extended context to 256K.
Post-training ran in 4 stages. SFT on 1.75T tokens came first, then mixed RL. Next, 6 specialist teachers were distilled into 1 model, followed by agentic RL.
How does LFM3-1.7B perform on benchmarks?
Liquid AI reports that LFM3-1.7B beats LFM2.5-2.6B with 35% fewer parameters. The bigger story is the agent results. On WebArena-Verified it scores 38.42, against 28.94 for Qwen3.5-9B. On BFCL V4 it scores 71.39, against 62.83.
Size still wins on knowledge and agentic coding. GPQA Diamond is 63.37, versus 80.40 for Qwen3.5-9B. SWE-bench Verified is 43.40, versus 51.20. Search narrows the gap: Humanity’s Last Exam rises from 7.17 to 13.90 with tools.
How do LFM3-1.7B and its rivals compare?
Sources: Liquid LFM3 model card, MiniCPM5-2B card, Qwen3.5-4B card, Gemma 4 E4B card, LFM license.
What about vision, audio, omni and decision models?
LFM3-VL-2B averages 71.4 across 62 vision benchmarks. Gemma 4 12B scores 71.7 on the same suite. It leads that comparison on visual grounding (77.3) and GUI understanding (83.1). Video (61.2) and agentic tasks (42.5) still trail larger API models.
LFM3-Audio-2B reaches 6.2 English WER and a 3.8 UTMOSv2 TTS score. LFM3-Omni-2B scores 70.1 on Daily-Omni, against 61.3 for Qwen2.5-Omni-7B.
d1-2B replaces d1-3B with a third fewer parameters. It improves Decision Bench from 83.6 to 85.2.
How can developers deploy LFM3?
Weights are on Hugging Face, the Liquid AI Playground and the Liquid API. The docs list GGUF, MLX and ONNX for the text and vision models. Runtimes include llama.cpp, MLX, vLLM, SGLang, Ollama and Transformers.
On-device, Liquid AI measured llama.cpp at Q4_0 with 32K context. A full 128K context needs 1.6 GB of KV cache. Qwen3.5-4B needs 4.3 GB. On 1 NVIDIA B300 with SGLang, LFM3-1.7B produces 3.6B tokens per day at 1,024 concurrent requests.
Liquid AI also showed specialization. A few hours of SFT and GRPO on 1 A100 lifted wind-turbine defect classification to 67%.
Key Takeaways
- 6 open-weight 2B-class models share 1 tri-hybrid backbone.
- LFM3-1.7B fits in about 1 GB at 4-bit with 256K context.
- It beats Qwen3.5-9B on BFCL V4, WebArena-Verified and AutomationBench.
- Knowledge and agentic coding remain clear weak spots.
- Free commercial use stops at $10M annual revenue.
Check out the technical details, model weights and docs. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us
Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
Auto-fetched and translated by AI Gem Box. All rights belong to the original author.