LiquidAI
PulseAugur coverage of LiquidAI — every cluster mentioning LiquidAI across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
-
LiquidAI releases compact LFM2.5-230M for on-device AI tasks
LiquidAI has released LFM2.5-230M, a compact language model designed for on-device deployment. This model boasts 230 million parameters and is optimized for efficient inference on various hardware, including CPUs and ed…
-
LiquidAI releases LFM2.5-230M-GGUF for local LLM deployment
LiquidAI has released the LFM2.5-230M-GGUF model, a compact language model designed for efficient local deployment. The model is compatible with a variety of popular inference libraries and applications, including llama…
-
20 small LLMs benchmarked on 6GB GPU for practical use
A user benchmarked 20 small language models on a 6GB RTX 4050 GPU to assess their practical utility for overnight tasks like file organization and log triage. The evaluation focused on qualitative tests and performance …
-
LiquidAI releases LFM2.5-8B-A1B model for on-device and server use
LiquidAI has released LFM2.5-8B-A1B, an 8 billion parameter model optimized for on-device applications and efficient server-side use cases. This model boasts a 128K context length and a hybrid MoE architecture, capable …
-
LiquidAI releases on-device LFM2.5-8B-A1B; OpenMOSS-TTS v1.5 adds languages
LiquidAI has released LFM2.5-8B-A1B, a new family of hybrid models optimized for on-device deployment, offering competitive performance with larger models and high inference speeds on both CPU and GPU. Concurrently, the…
-
Liquid AI launches Japanese text and audio LLMs
Liquid AI has released two new Japanese-language models: LFM2.5-1.2B-JP-202606, a text-based chat model that shows improved performance in knowledge, instruction following, and coding, and LFM2.5-Audio-1.5B-JP, a multim…
-
LiquidAI releases LFM2.5 embedding and ColBERT retrieval models
LiquidAI has released two new multilingual retrieval models: LFM2.5-Embedding-350M, a dense bi-encoder for fast indexing, and LFM2.5-ColBERT-350M, a late-interaction model for higher accuracy. Both models have 350 milli…