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LFM2.5-VL-3B
LFM2.5-VL-3B is Liquid AI's 3.1B-parameter vision-language model for on-device image understanding, grounding, object detection, OCR, and tool use.
It combines the LFM2.5-2.6B language backbone with a SigLIP2 NaFlex vision encoder, supports 16 languages, and comes with native, GGUF, ONNX, and MLX paths for browser, mobile, desktop, edge, and server experiments. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A compact model that reads images and text
The model accepts text and images, preserves varied image shapes through a dynamic vision encoder, and can split larger images into patches while keeping a whole-image view for context.
Why it stands out
Grounding and page layout are first-class outputs
Beyond describing an image, LFM2.5-VL-3B can return object boxes from natural-language queries and experimental OCR layout annotations for text, tables, equations, images, headers, footers, and other page regions.
Availability
Browser demo and several local formats
Readers can try the official WebGPU Space or Liquid playground, download the main checkpoint, or inspect GGUF, ONNX, and MLX variants for llama.cpp, cross-platform acceleration, and Apple Silicon.
Why it matters
What makes it useful
This model puts several visual jobs into a footprint intended for local hardware: read a document page, locate an object from a phrase, translate a sign, describe an image, or pass a structured result into a tool. Readers can test whether those tasks can stay on their own device before reaching for a larger model or a hosted vision API.
What to know
Where it fits
Liquid AI recommends it for single-turn, high-throughput, low-latency work such as near-real-time object detection, scanned-document OCR with layout information, and on-device sign or menu translation. The model card does not recommend it for long-context, reasoning-heavy work such as visual web design or highly technical blueprint questions.
Notable points
What stands out
The speed, memory, throughput, and benchmark figures come from Liquid AI's own tests on named hardware and software setups. The OCR layout format is marked experimental and may change or produce unreliable output, so use the published results as a starting point and test the exact device, format, image types, and prompts that matter.
Before using
What to review
Choose the runtime and weight format for the device: Transformers, vLLM, SGLang, GGUF with llama.cpp, ONNX, or MLX.
Check memory, speed, image resolution, patch count, and output quality with the intended quantization and workload; publisher results depend on specific hardware and settings.
Compare OCR text, reading order, tables, equations, and bounding boxes against the source page before using them in search, RAG, automation, or records.
Treat the layout-annotation format as experimental, and keep a parser or workflow ready for missing, malformed, or changed regions.
Keep tool execution separate from model output: validate arguments, restrict permissions, and require approval before a generated call changes anything important.
The model card names the LFM Open License v1.0. Review the current terms at the main source for the intended use.
For private images, check the full app and runtime path rather than assuming downloadable weights make every demo, interface, tool, or connection local.
Reader fit
Who may find it relevant
Builders testing local or edge OCR, image grounding, object detection, visual question answering, or tool-assisted image workflows.
Teams comparing browser, Apple Silicon, CPU, and GPU paths for one compact vision-language model.
People who want downloadable weights and several deployment formats rather than only a hosted vision endpoint.
Less relevant for readers who need a no-setup consumer app, very long visual context, or deep reasoning over technical drawings.
Editorial note
Why LifeHubber lists it
LFM2.5-VL-3B brings document OCR, object grounding, multilingual image work, and tool calls into one compact model with several local run paths. That gives readers a practical way to compare what can happen on their own hardware, where a larger model is still needed, and which outputs need checking before they enter another workflow.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Separate the visual model from the wider local setup.
Continue with the text-only sibling when long context and agent work matter more, or compare the wider range of local and self-hosted routes.
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