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Liquid AI Releases the LFM3 Series of Models, Including 1.7B Text, Vision, Audio, and Omni Versions

Liquid AI has released the LFM3 series of open-weight models, all branched from LFM3-1.7B-Base, covering text, vision, audio, Omni, and the d1 decision model. LFM3-1.7B-Base adopts a three-hybrid-layer architecture, is pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens incorporates more agent trajectories and tool-calling data, with the context window extended to 256K tokens. The company says LFM3-1.7B is about 25% faster than LFM2-1.2B at an 8K context, and scores 38.42 versus 3.02 on WebArena-Verified; LFM3-VL-2B's average score across 62 vision benchmarks rises from 57.6 to 71.4. All models are available for download on Hugging Face, along with a single-GPU fine-tuning notebook.

Block launches full-duplex speech model Dessa, supports local operation

Block has launched the full-duplex speech model Dessa, which can listen to the user's speech while speaking, and can simulate emotional expressions such as laughter, pauses, and choking up. The company says it is trained entirely on human speech data, using only a single rack of NVIDIA GPUs for training, and can run locally on MacBooks with M4 Max and higher configurations. At this stage Dessa is not open for public trial; Block is sending testing invitations to security researchers, focusing on risk scenarios in which speech agents deceive humans.

Meta and Apple Partner to Release Open-Weight Model Muse Spark Open

Meta has released the open-weight model Muse Spark Open and worked with Apple's hardware team to enable it to run efficiently on the M5 Mac Studio Ultra. The model has 42 billion active parameters, a 1 million token context window, and multimodal capabilities. Meta says it performs well on agentic and coding tasks, and the weights have been made public on HuggingFace under Meta's Muse Model license. Running the model on an Apple computer requires two Mac Studios equipped with the M5 Ultra, each starting at $5,499.

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Ant Ling Open-Sources Ling-3.0-tiny: 7.9B Total Parameters, 1.3B Activated Parameters

Ant Ling has officially open-sourced Ling-3.0-tiny, a native hybrid reasoning model with 7.9B total parameters that activates only 1.3B parameters during inference, available in BF16, FP8, and INT4 versions. The company says it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with output speed exceeding 160 tokens/s. The model can run FP8 inference on a single NVIDIA DGX Spark with weights of about 7.85GB, and has been adapted for local execution on Apple Silicon MacBook and Mac mini.