LFM2.5-VL-450M Zero Config

The fastest way to get this model running locally is via Docker.

Just follow the guidelines provided below.

There is no manual tuning required; the builder will automatically deploy the best matching configuration.

🧩 Hash sum → 606f1a066a30935187689847f8f32249 — Update date: 2026-06-28 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS overhead Disk Space: free: 80 GB on system drive for scratch space Graphics: 12 GB VRAM minimum required for basic quantization

The LFM2.5-VL-450M is a state‑of‑the‑art multimodal language model that combines advanced vision and language understanding in a single unified architecture. It leverages a large‑scale contrastive pre‑training regimen that aligns image embeddings with textual representations, enabling precise cross‑modal retrieval. With 450 million parameters, the model achieves competitive performance on benchmark datasets while maintaining a relatively small memory footprint. Its design incorporates a hierarchical attention mechanism that dynamically focuses on salient visual regions and contextual words, improving coherence in generated captions. The model supports real‑time inference on consumer‑grade hardware and is optimized for integration into applications requiring robust visual‑language tasks such as image captioning, visual question answering, and content moderation. It was trained on a diverse collection of publicly available image‑text pairs and curated domain‑specific datasets, ensuring broad coverage and reduced bias.

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