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Liquid AI shares a recipe for upgrading a pretrained model's tokenizer in place, expanding LFM2.5's tokenizer from 65K to 128K to improve efficiency for languages like Thai, Vietnamese, and Hindi, resulting in up to 4x fewer tokens and 2.2-3.7x faster generation.
A Substack article explains the 'doom loop' problem in LLMs where models repeat tokens endlessly, and introduces Final Token Preference Optimization (FTPO) from Liquid AI as a method to detect and fix such loops during fine-tuning.
Liquid AI celebrates processing 1 billion requests on Shopify’s platform, highlighting a milestone in their multi-year partnership.
Liquid AI releases Antidoom, an open-source method to reduce doom loops in reasoning models, applied to LFM2.5-2.6B and Qwen3.5-4B, significantly lowering doom-loop rates and improving eval scores.
ifstruct is an instruction-following benchmark for structured output by Liquid AI, designed to push the field toward better small models that can run locally.
Fine-tuned LiquidAI's LFM2.5-230M model on Fable-5 coding traces, finding the results better than expected.
Open source AI inference reaches 300 tok/s on mobile, with a WebGPU framework pushing Liquid AI's LFM2.5 230M to 1,400 tok/s in browser.
LiquidAI releases LFM2.5-230M, a 230M parameter language model designed to run on limited hardware, with support for transformers, vLLM, and SGLang.
You can now train Liquid AI's LFM2-VL model using TRL's GRPO and RLOO methods, with an example script provided.
Liquid AI's LFM2.5-230M model demonstrates multi-step tool-calling capabilities on a Unitree G1 robot, running entirely on-device on an NVIDIA Jetson Orin, acting as a skill-selection layer.
Liquid AI released LFM2.5-230M, a compact 230M-parameter hybrid model optimized for on-device deployment with fast edge inference speeds (213 tok/s on Galaxy S25 Ultra) and built for agentic tasks via reinforcement learning.
Liquid AI demonstrates using LFM2.5-ColBERT-350M as a filter to select only the five most relevant tools from 151 options, reducing latency and improving tool selection accuracy.
Liquid AI released LFM2.5-8B-A1B, an edge MoE model trained on 38T tokens with a 128K context window, improved tool calling, and reasoning capabilities, available on Hugging Face.
Liquid AI released LFM2.5-8B-A1B, an edge model with a 128K context window, 38T tokens of pre-training, and large-scale reinforcement learning, capable of tool calling and complex tasks while fitting on an entry-level laptop.
Liquid AI releases LFM2.5-8B-A1B, an 8B MoE model with 1.5B active parameters and 128K context, optimized for edge devices.
LottoLabs announces LiquidAI's LFM2.5-8B-A1B-GGUF model, an 8B parameter model trained on a massive token count and optimized for fast inference on limited GPU hardware, with support for llama.cpp, Ollama, vLLM, and more.
LiquidAI releases a GGUF quantized version of their LFM2.5-8B-A1B model, with instructions for use across multiple inference engines.
LiquidAI releases LFM2.5-ColBERT-350M, a late-interaction multilingual retrieval model, along with a dense bi-encoder variant, both built on LFM2.5-350M-Base, supporting 11 languages and designed as drop-in replacements for RAG pipelines.
A tweet argues the next AI boom will be compact intelligence on edge devices rather than larger data centers, with Liquid AI supporting the vision of running AI on phones, cars, and everyday devices.
Liquid AI releases LFM2.5-Embedding-350M, a dense bi-encoder for multilingual retrieval supporting 11 languages, as a drop-in replacement for RAG pipelines.