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View details ↗DISCOVER & COMPARE
Explore AI models 13
MedPsy-1.7B
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View details ↗MedPsy-4B
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View details ↗MedPsy-4B-GGUF
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View details ↗**VisionPsy-Nano-460M-Flash-GGUFs** provides GGUF weights of VisionPsy-Nano-460M-Flash for fast, fully on-device multimodal inference with our patched **llama.cpp** fork (not stock upstream). Flash is the **efficiency-optimized** sibling of VisionPsy-Nano-460M: same ~460M nanoVLM architecture, but it processes each image with **far fewer visual tokens (as few as 64 at 512x512)**, so it responds an order of magnitude faster on real phones while giving up only a small amount of accuracy. Unquantized **FP32** and **BF16 GGUFs** are included alongside a family of quantization formats, from near-lossless **8-bit** through a high-quality **5-bit** option down to ultra-compact **3-bit**. All sub-8-bit variants (except the legacy `Q4_0`) use **importance-matrix (imatrix) calibration**. If VisionPsy-Nano-460M-GGUF is our best-quality ~0.5B GGUF, **Flash-GGUF is the one you ship when time-to-first-token, battery, and RAM are the binding constraints.** > Each GGUF is a **vision-language** model: alongside the text weights (`*.gguf`) you also need the > multimodal projector (`mmproj-*.gguf`, the SigLIP2 vision encoder) to run image inputs. See > Usage.
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View details ↗**VisionPsy-Nano-460M-GGUFs** provides GGUF weights of VisionPsy-Nano-460M for fast, fully on-device multimodal inference with our patched **llama.cpp** fork (not stock upstream). Requires the patched llama.cpp. VisionPsy-Nano-460M is a compact (~460M parameter) vision-language model that **compares favourably with other models in its ~0.5B weight class on 16 of 17 public benchmarks**; these GGUF builds bring that quality to phones, tablets, and edge hardware. We release some **GGUF variants**, ranging from unquantized **FP32** and **BF16** references to lossless **8-bit**, high-quality **5-bit**, and ultra-compact **3-bit** formats. This range supports deployment across diverse hardware settings, from high-end workstations to resource-constrained on-device environments. All sub-8-bit variants (except the legacy Q4_0) use **importance-matrix (imatrix) calibration**, which consistently reduces quality degradation. > Each GGUF is a **vision-language** model: alongside the text weights (`*.gguf`) you also need the > multimodal projector (`mmproj-*.gguf`, the SigLIP2 vision encoder) to run image inputs. See > Usage.
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