MODEL PROFILE · 模型档案

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.

译文排队中,当前展示来源原文。

视觉问答multimodallicense:apache-2.0
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:16:19
OVERVIEW

模型介绍

**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.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

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。