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AI 模型库/qvac/VisionPsy-Nano-460M-Flash-GGUFs
MODEL PROFILE · 模型档案

VisionPsy-Nano-460M-Flash-GGUFs

qvac · VisionPsy-Nano-460M-Flash-GGUFs

**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.

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2026/10/30 15:12:38
OVERVIEW

模型介绍

**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.

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

huggingface · qvac/VisionPsy-Nano-460M-Flash-GGUFs ↗2026/10/30 00:53:39
huggingface · enrich:45431af4-f74d-4f42-89f0-5be56e7c3b60 ↗2026/10/30 15:12:38

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。