LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge
热度趋势
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官方发布带来Hugging Face 模型更新信号，适合跟踪能力变化、生态影响和后续落地。
LFM2.5-VL-3B 是一款专为设备端和实时应用设计的视觉语言模型，为边缘计算提供增强的视觉能力。该模型能够理解文档和屏幕、定位物体并调用工具，它提供直接答案而非推理以保持速度。…
LFM2.5-VL-3B is our most capable vision-language model you can run on your own hardware. It understands documents and screens alike, grounds objects, and can call tools. It answers directly instead of reasoning, so responses stay fast in real-time and on-device apps.
LFM2.5-VL-3B extends the vision-language capabilities of our previous releases with four major improvements:
- Screen/UI understanding: Strong understanding of digital screens across different devices.
- Grounding: Improved grounding and object detection with natural language queries.
- Multi-image input: Improved reasoning across multiple images.
- Function calling: Significantly stronger at function calling, in text-only and vision-text situations.
How we trained our most capable vision-language model
LFM2.5-VL-3B pairs a SigLIP2 400M NaFlex vision encoder with the same pre-trained backbone as our LFM2.5-2.6B text model. It is pre-trained on about 34T tokens, with 4x more vision data than before, drawn from curated and synthetic image-caption, OCR, grounding, and instruction-following sets. To support non-Latin scripts, we doubled the vocabulary to 128K by extending the tokenizer in place rather than retraining from scratch.
Post-training runs in two stages: First is supervised fine-tuning (SFT), with knowledge distillation from a larger teacher and Antidoom training . Second is multi-reward reinforcement learning (RL).
Benchmark results
We evaluated LFM2.5-VL-3B across both vision and text benchmarks.
The vision benchmarks cover multilingual visual comprehension, instruction following, visual math and scientific reasoning, document understanding, object detection, multi-image understanding, and screen understanding. LFM2.5-VL-3B leads its size class on real-world image tasks, while also reading digital content well, from documents and charts to on-screen UI elements.
Task Benchmark LFM2.5-VL-3B (3.1B) LFM2-VL-3B (3.1B) gemma-4-E2B-it (5.1B) gemma-4-E4B-it (8B) InternVL 3.5 2B (2.4B) InternVL 3.5 4B (4.7B) Qwen3.5-2B (2.3B) Qwen3.5-4B (4.7B)
General MMStar 63.3 57.7 45.3 52.9 57.7 65.5 55.1 59.3
MME 73.1 73.0 54.9 67.6 73.6 81.0 76.2 79.5
RealWorldQA 73.1 71.1 60.0 64.3 61.6 67.7 65.1 67.1
SimpleVQA 35.4 33.0 27.3 30.4 30.5 33.7 35.2 40.7
SEED-Bench (image) 77.7 76.6 71.4 75.3 75.4 76.4 75.8 76.1
MMBench (dev EN v1.1) 81.0 80.0 64.2 71.6 76.2 81.1 73.1 78.4
CountBenchQA 87.3 92.2 70.4 80.5 70.4 82.5 83.8 86.7
Multilingual MMMB 83.0 81.9 73.3 80.4 76.3 81.5 75.9 82.0
Multilingual MMBench 79.5 76.3 62.8 71.2 70.9 76.6 69.9 77.0
Multimodal IF MM-IFEval 60.6 51.4 65.6 68.2 47.1 54.5 55.4 63.1
STEM LogicVista 37.4 32.2 29.5 34.5 30.9 36.2 34.0 37.6