Local Ai
LiquidAI/LFM2.5-VL-3B · Hugging Face
LFM2.5-VL-3B is a multimodal variant of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on LFM2-VL-3B with further mid- and post-training. LFM2.5-VL-3B can process both
LFM2.5-VL-3B is a multimodal variant of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on LFM2-VL-3B with further mid- and post-training. LFM2.5-VL-3B can process both text and images, and uses the LFM2.5-2.6B language model as its backbone, combined with a SigLIP2 NaFlex vision encoder. Better grounding: Improved grounding and object detection with natural language queries. Better OCR: Full page OCR with layout annotation. See layout annotation format for more information. Efficient inference: 228 tok/s on an Apple M5 Max and 116 tok/s on an AMD Ryzen AI Max+ 395, in under 3.3 GB of memory. Find more information about LFM2.5-VL-3B in our release post. Model Details: LM Backbone: LFM2.5-2.6B Vision encoder: SigLIP2 NaFlex shape‑optimized 400M Vocabulary size: 128,000 Context length: 32,768 tokens Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish Native resolution processing: Uses SigLIP2's NaFlex; large images are split into non-overlapping 512×512 patches and a resized whole-image thumbnail. Generation parameters: text: temperature=0.2, top_k=50, repetition_penalty=1.0 vision: Use the processor_config.json file. We recommend using it for single-turn, high-throughput, low-latency tasks; for example, for near-realtime object detection in automotive applications, batch processing scanned documents with OCR with layout information for turning PDFs into searchable text, or for on-device translation of menus and road signs into your native language. It is not recommended for long-context, reasoning-intensive tasks, such as visual web design, or answering highly technical questions about blueprints. On-device Inference LFM2.5-VL-3B decodes 228 tokens/s on an Apple M5 Max and 116 tokens/s on an AMD Ryzen AI Max+ 395, and fits in about 3 GB of memory. It even reaches 20 tokens/s on a Galaxy S26 Ultra, so you can run it fully on-device. GPU Inference On a single NVIDIA H100 with vLLM, LFM2.5-VL-3B reaches the highest output throughput of any model we tested, about 11K tokens per second at high concurrency, or nearly 1B tokens per day. submitted by /u/pmttyji [link] [comments]
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Source: r/LocalLLaMA | 2026-08-12