Liquid Audio - Speech-to-Speech audio models by Liquid AI
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Updated
Jun 5, 2026 - Python
Liquid Audio - Speech-to-Speech audio models by Liquid AI
Running Mixture of Agents on CPU: LFM2.5 Brain (1.2B) + Falcon-R Reasoner (600M) + Tool Caller (90M). CPU-only, 16GB RAM. Lightweight AI Legion.
A hackable library for running and fine-tuning modern transformer models on commodity and alternative GPUs, powered by tinygrad.
LFM2-powered research agent | Automated Related Work & citations | Semantic Scholar + arXiv
Advanced PDF/Document Translator with interactive comparison. Built on IBM Docling.
Two-layer agentic Earth Observation on satellite-class compute. Fine-tuned LFM2.5-VL perception + tool-calling agent over Sentinel-2 imagery. Galamsey detection in Ghana as the worked example.
ComfyUI nodes for Batch Image Captioning + various Vision-Language (VL) models, including InternVL3.5, Xiaomi MiMo-VL, LiquidAI LFM2-VL, Kwai Keye-VL, AIDC-AI Ovis2.5 and Ovis-U1. Models: Ovis2.5-2B, Ovis2.5-9B, Keye-VL-8B-Preview, MiMo-VL-7B-RL-GGUF, LFM2-VL-450M, LFM2-VL-1.6B, Ovis-U1-3B, Ovis2.5-2B, Ovis2.5-9B, InternVL3_5-1B/2B/4B/8B/14B/38B
Fine-tuning LFM2-1.2B for Korean-English bidirectional translation. GRPO+COMET & SFT Training, outperforming 4B models.
🎯 The world's first fully internationalized LFM2-350M chat interface with native support for 8 languages!
Fine-tune LiquidAI LFM2.5 Embedding models for dense retrieval using your own data
PocketLFM — run Liquid AI's LFM2.5 large language model fully on-device on Android. Offline, private, no cloud. Open-source edge AI via llama.cpp (GGUF). First independent open-source Android app to run LFM2.5 outside Liquid's own Apollo/LEAP.
Offline RAG study assistant powered by LFM2-2.6B · Ask questions, get exercises from any PDF textbook · No cloud required
A minimal implementation of Liquid Foundation Model LFM 2.5 2.6B model
Fine-tuned 350M browser-agent model (LFM2.5-350M). 91.2% action accuracy on Mind2Web, runs in-browser via wllama WebAssembly.
Minimal android app to make inference of llm models
a local-first pipeline to diarise any audio file, supporting multiple codecs thanks to FFMPEG. Includes an identification pipeline to identify the speakers and classify male and female voices.
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