MODEL PROFILE

VisionPsy-Nano-460M-GGUFs

qvac · VisionPsy-Nano-460M-GGUFs

**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.

Visual QAmultimodallicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:19
OVERVIEW

Model overview

**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.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。