Abstract
https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF from liquidAI: LFM3-1.7B is a text model for local agents and specialized applications , based on our new tri-hybrid architecture. Agentic training: post-trained for agentic harnesses such as Hermes Agent, Pi, and OpenClaw, with a 256K-token context window. Designed for the edge: at 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop. Made to be specialized: adapt the model to your domains, formats, and tools via quick single-GPU fine-tuning. Find more information about the LFM3 family in our release blog post . Model details The LFM3 family includes base, text, vision, audio, omni, and decision models. Property Specification Checkpoint Post-trained, text-only language model Parameters 1.73B Architecture 24 layers: 13 gated short convolutions, 5 sliding-window attention, 6 global attention Context length 256K tokens (input + output) Vocabulary 128,000 tokens Languages English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish License LFM Open License v1.0 LFM3-1.7B is designed for local agents, tool calling, structured outputs, and domain-specific fine-tuning. Use retrieval or search for knowledge-heavy tasks. It is not recommended for agentic coding or complex multi-step reasoning due to its small size. more LFM3 to enjoy today: (my initial idea was to create 2 posts but they released six models) https://huggingface.co/LiquidAI/LFM3-VL-2B https://huggingface.co/LiquidAI/LFM3-VL-2B-GGUF LFM3-VL-2B is a vision-language model for on-device visual understanding . It takes text, images, and video as input, and combines the LFM3 language model with a new in-house vision encoder. New in LFM3-VL-2B: Video understanding: the first LFM vision model that takes video as input. Ask questions about a recorded lecture, a walkthrough filmed on your phone, or a clip from a camera feed. Thinking: the model reasons step by step before it answers. This helps most with harder visual questions, such as math, charts, and multi-step problems. Spatial and embodied reasoning: the model finds objects and reasons about how they are placed relative to each other, a strong foundation for robotics applications. https://huggingface.co/LiquidAI/LFM3-Audio-2B LFM3-Audio-2B is a compact, fully featured audio model that runs locally on laptops and edge devices . It takes text or audio as input and responds with text, speech, or both. Voice chat: ask questions aloud and receive streaming replies in text and speech. For text-only responses, the model can reason before answering. Multilingual speech: transcribe, synthesize, and translate speech across 18 languages. Audio understanding and reasoning: interpret recordings and answer questions about the speech, sounds, and events they contain. Tool calling: turn spoken requests into tool calls to control apps and devices, with text or speech alongside the calls. LFM3-Audio-2B introduces an in-house audio encoder and LiquidCodec audio tokenizer, improving speech quality over LFM2.5-Audio. https://huggingface.co/LiquidAI/LFM3-Omni-2B LFM3-Omni-2B is Liquid AI 's first omni-modal model in the LFM3 family: a single end-to-end model that understands text, images, video and audio , and answers in text and speech . It is built for on-device use: one model replaces a separate ASR model, vision-language model, LLM and TTS model. Any input, text or speech output: ask about a photo by voice, transcribe or translate speech, chat out loud, or ask questions about a video and its soundtrack together. Watches and listens together: for videos with sound, the model sees and hears them at the same time, so it can answer questions that need both. Small enough for devices: at 2.2B parameters, it is designed to run efficiently on phones, laptops and other edge hardware. https://huggingface.co/LiquidAI/d1-Omni-2B d1-Omni-2B is a decision model for text, pictures, and audio , built on LFM3-Omni-2B . It returns calibrated answers in one forward pass, without generating tokens. Native audio: clips and spoken questions go through the same pass as text and pictures, and spoken questions score within 0.5 points of written ones on unseen tasks. Beyond classification: it also extracts text, numbers, and dates with their span, locates objects in pictures, and estimates quantities with an interval. Millisecond decisions: 5.8 ms per decision on an NVIDIA H100 and 3.7 ms on an AMD MI325X, or 12.4 ms and 8.2 ms with a 10-second clip. https://huggingface.co/LiquidAI/d1-2B d1-2B is a decision model for text, pictures, and video , built on LFM3-VL-2B . It returns calibrated answers in one forward pass, without generating tokens. Best-in-class: it leads the open d1 models on average and on our board of over a hundred decision benchmarks, ahead of d1-3B at under two thirds of its size. Beyond classification: it also extracts text, numbers, and dates with their span, locates objects in pictures, and estimates quantities with an interval. Millisecond decisions: 5.7 ms per decision on an NVIDIA H100 and 3.8 ms on an AMD MI325X, with any number of questions about one state in one call. https://huggingface.co/LiquidAI/LFM3-1.7B-Base LFM3-1.7B-Base is the pre-trained checkpoint behind LFM3-1.7B , a text model for local agents and specialized applications based on our new tri-hybrid architecture. Pre-trained on 22T tokens: the last 3.5T tokens are curated mid-training data with agentic traces and tool calls, and extend the context window to 256K tokens. Designed for the edge: at 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop. Made to be specialized: a starting point for continued pre-training and your own post-training. llama.cpp support has been merged: https://github.com/ggml-org/llama.cpp/pull/31257 https://github.com/ggml-org/llama.cpp/pull/31252 https://github.com/ggml-org/llama.cpp/pull/31275 submitted by /u/jacek2023 to r/LocalLLaMA [link] [comments]