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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.
Liquid AI releases DSpark draft models for their LFM series, incorporating speculative decoding to achieve up to 4x decode speedup on device while maintaining output quality.
Liquid AI released LFM2.5-VL-3B, a 3.1B vision model that runs locally on an iPhone 17 and can recognize objects like a Steve toy from Minecraft, with significantly improved spatial grounding (ScreenSpot-v2 desktop from 6 to 78.7).
A user shares surprising results testing Liquid AI's new LFM2.5-VL-3B vision-language model across many languages, noting strong visual capabilities but weaker instruction following; Liquid AI announces the model can read screens, documents, and ground objects to coordinates.
A cookbook repository by Liquid4All with examples, end-to-end tutorials, and applications for building with Liquid AI's open-weight LFMs and the LEAP SDK on laptops, mobile, and edge devices.
A detailed report on quantizing LiquidAI's LFM2.5-2.6B model with various GGUF and KV cache quantizations, showing it fits on an 8GB Raspberry Pi with minimal degradation, but warning against Q4_K_M.
Daniel van Strien puts Liquid AI's 2.6B LFM2.5 model to work on Hugging Face as a 'librarian bot' that investigates datasets with tools and writes one-sentence summaries, with plans to let it propose dataset-card PRs once the community provides 500 ratings.
MacPaw partners with Liquid AI to bring on-device AI inference and local memory to its products and app store, planning to offer the tech stack to developers and introduce credit-based AI pricing.
Liquid AI announces a partnership with MacPaw to bring on-device AI to Mac users, designing specialized Liquid Foundation Models for macOS AI assistance.
Liquid AI released LFM2.5-2.6B, a 2.69B parameter model with 128K context and tool calling, optimized for multi-step agent workflows and capable of running at 30 tok/s on a phone with a 1.67GB Q4_K_M GGUF, though coding and knowledge-heavy tasks remain weak compared to larger models.
Liquid AI released LFM2.5-2.6B, a new tiny model focused on agentic capabilities. The author is excited to test it for high-volume tasks like document summarization.
Liquid AI's head of post-training explains how to build a sub-1GB on-device model in 20 minutes using LFM2.5, on-policy preference alignment, agentic RL, curriculum training, and iterative model merging, achieving tool-calling reliability that beats much larger models.
Liquid AI fine-tuned their LFM2.5-Encoder models (230M and 350M) to perform multi-label classification in a single forward pass, eliminating the need for decoding loops or parsing. This demonstrates efficient label scoring for NLP tasks.
Liquid AI released LFM2.5-2.6B, a 2.6B-parameter hybrid model optimized for on-device deployment with 128K context, agentic post-training, and fast inference (220 tok/s on Apple M5 Max) under 2.5GB memory.
Liquid AI releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, bidirectional encoders optimized for non-generative tasks like classification and retrieval, offering fast CPU inference at long context.
Liquid AI released two new encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, that are fast, easy to train, and strongly multilingual, with speed benchmarks showing over 3.7x improvement on CPU compared to ModernBERT-base.
Liquid AI releases LFM2.5-Encoders (230M and 350M), efficient encoder models optimized for long-context inference on CPU, matching or beating larger encoders on benchmarks with 3.7x speedup over ModernBERT-base.