LFM2.5 VL 3B: Compact Vision-Language Model from Liquid AI
LFM2.5 VL 3B is a 3.1B-parameter vision-language model from Liquid AI. It reads images and text and answers in text. It pairs the LFM2.5-2.6B hybrid language model with a SigLIP2 NaFlex vision encoder, and builds on LFM2-VL-3B with further mid- and post-training.
Key Features
- Grounding and object detection - Finds objects from natural-language queries and returns boxes as normalized coordinates
- Full-page OCR with layout - Parses documents into labelled regions such as text, titles, tables, charts and equations
- Native resolution - Large images are split into 512x512 patches plus a whole-image thumbnail instead of being squashed
- Tool calling - Writes Pythonic function calls that the server returns as standard tool calls
- Multilingual - Supports 16 languages, including English, Chinese, Japanese, Korean, Arabic, Hindi and the main European languages
- 32K context - Room for multi-image prompts and long documents
Use Cases
- Batch OCR of scanned documents into searchable, structured text
- Screen and UI understanding
- Near-realtime object detection and grounding
- Translating photographed menus and signs
- Visual question answering and image description
Architecture
The language backbone is LFM2.5-2.6B, a hybrid model where most layers are short gated convolutions and a minority are grouped-query attention. That keeps memory for long context small. The vision side is a 400M SigLIP2 NaFlex encoder. The vocabulary is 128,000 tokens.
The model answers directly rather than reasoning step by step, so time to first token is short. Liquid recommends it for single-turn, high-throughput, low-latency tasks, and not for long-context, reasoning-heavy work.
Performance
Liquid reports large gains over LFM2-VL-3B in screen understanding, grounding, multi-image input and tool use: 80.7 on ScreenSpot-v2, 87.9 on RefCOCO, 61.5 on BLINK, 58.3 on MuirBench and 59.5 on ToolSandBox. On general vision benchmarks it scores 73.1 on RealWorldQA, 81.3 on ChartQA, 68.5 on MathVista and 88.7 on POPE.
Recommended text sampling is temperature 0.2 and top_k 50.
Learn more on the model card.
