LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge
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.
- ▪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.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Hugging Face Blog |
| Canonical URL | https://huggingface.co/blog/LiquidAI/lfm2-5-vl-3b |
| Publication time | Wed, 12 Aug 2026 14:00:51 GMT |
| Retrieval time | 2026-08-12T14:01:37.479Z |
| Last seen | 2026-08-12T14:01:37.479Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | aWsVQ915Y7Ua · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Back to Articles LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge Team Article Published August 12, 2026 Upvote - Samuel Stevens samuelstevens Follow LiquidAI Ryan Shubert shubeydoo Follow LiquidAI Sina s-jse Follow LiquidAI Tianshu Yu tianshu-yu Follow LiquidAI Brandon Brandon3967 Follow LiquidAI Leonie Monigatti iamleonie Follow LiquidAI How we trained our most capable vision-language model Benchmark results Inference speed on CPU and GPU How to use LFM2.5-VL-3B LFM2.5-VL-3B demo Get Started Citation 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hugging Face Blog.
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