LiquidAI/Text / LLMRuns anywhere
Liquid AI debuts LFM3-1.7B for edge deployment
A compact 1.7B-parameter multilingual model built to run locally and serve as the base for specialized multimodal variants.
Company
LiquidAI
8 models
Releases
LiquidAI/Any-to-AnyRuns anywhere
Liquid AI's LFM3-Omni-2B brings any-to-any AI to the edge
The 2-billion-parameter model combines vision, audio and speech-to-speech in a package small enough to run on device.
LiquidAI/EmbeddingsConsumer GPU
Liquid AI's LFM2.5 Puts a 24B MoE on Your Device
The new mixture-of-experts model keeps total capacity high while activating only about 2B parameters per token for edge agents.
LiquidAI/Vision-LanguageRuns anywhere
Liquid AI's d1-3B brings multimodal models to the edge
The new LFM2-based d1-3B is a compact vision-language model aimed at running decisions directly on-device.
LiquidAI/Vision-LanguageRuns anywhere
Liquid AI's LFM2.5-VL-3B targets on-device vision
The 3-billion-parameter vision-language model is tuned for faster multimodal work on edge hardware.
LiquidAI/EmbeddingsRuns anywhere
Liquid AI ships LFM2.5, a 2.6B on-device model
The latest Liquid Foundation Model targets multilingual text generation on laptops and phones, packaged in GGUF for local runtimes.
LiquidAI/EmbeddingsRuns anywhere
Liquid AI's LFM2.5-2.6B targets on-device agents
A compact 2.6-billion-parameter model built to run local, multilingual agents without leaning on the cloud.
LiquidAI/EmbeddingsRuns anywhere
Liquid AI's LFM2.5 encoder targets fast CPU inference
A 230M-parameter bidirectional encoder built for long-context English and German embeddings without a GPU.
LiquidAI/EmbeddingsRuns anywhere
Liquid AI ships a 350M encoder built for CPUs
The compact LFM2.5 encoder targets fast, long-context text embeddings without a GPU.
LiquidAI/Text / LLMRuns anywhere
Liquid AI's LFM2.5-230M targets on-device language tasks
A 230-million-parameter multilingual model built to run efficiently at the edge rather than in the cloud.
LiquidAI/EmbeddingsRuns anywhere
Liquid AI ships a 350M multilingual embedding model
LFM2.5-Embedding-350M targets retrieval and search workloads on edge hardware, where compact size matters as much as accuracy.