OpenBMB发布MiniCPM-V-4.7-1B多模态模型
openbmb/MiniCPM-V-4.7-1B
推荐理由
端侧多模态模型持续突破性能边界，1B参数实现高效能密度，适合关注本地部署与移动端的开发者参考。
Gguf's: https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf
Gguf格式模型：https://huggingface.co/openbmb/MiniCPM-V-4.7-1B-gguf
"MiniCPM-V 4.7 is the latest model in the MiniCPM-V series, available in a 1B variant. It builds on the family's single-image, multi-image, and video understanding capabilities, combining stronger foundation capabilities with efficient visual encoding and on-device inference. Notable features of MiniCPM-V 4.7 include:
"MiniCPM-V 4.7 是 MiniCPM-V 系列中的最新模型，提供 1B 参数版本。它在家族的单图、多图和视频理解能力基础上，结合了更强的基础能力与高效的视觉编码及端侧推理技术。MiniCPM-V 4.7 的显著特点包括："
- 🔥 A New Pareto Frontier in Performance and Efficiency. As model capabilities continue to advance, inference efficiency has become an important measure of a model's value. On resource-constrained devices, delivering the strongest possible capabilities within a limited resource budget calls for higher capability density. The 1B variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier for MLLMs. In Instruct mode, the 1B variant continues to improve foundation capabilities over MiniCPM-V 4.6 and substantially outperforms Qwen3.5 0.8B in both overall score and inference efficiency.
- 🚀 Efficient Architecture. Based on the intra-ViT early compression technique from LLaVA-UHD v4, MiniCPM-V 4.7 compresses visual tokens early within the vision encoder, reducing computation in subsequent encoder layers. It also supports mixed 4x/16x visual token compression, allowing developers to balance visual detail and inference cost across tasks.
- 💻 Broad Platform and Chip Support. MiniCPM-V 4.7 supports iOS, Android, and HarmonyOS, together with open-source on-device deployment support. It also provides day-0 support for chip platforms from Rockchip, Houmo, Intel, AMD, and Arm, extending deployment options across mobile and local computing devices.
- 🛠️ Developer Friendly. MiniCPM-V 4.7 supports inference frameworks including SGLang, vLLM, llama.cpp, and Ollama, along with fine-tuning frameworks including SWIFT and LLaMA-Factory. It provides GGUF, BNB, AWQ, and GPTQ quantized variants and adds FP8 quantization support. We also release Self-Replay Fine-Tuning, a fine-tuning approach that helps developers extend model capabilities at low cost while reducing forgetting of existing capabilities, making further customization easier."
- 🔥 性能与效率的新帕累托前沿。随着模型能力的不断进步，推理效率已成为衡量模型价值的重要指标。在资源受限的设备上，要在有限的资源预算内提供尽可能最强的能力，就需要更高的能力密度。MiniCPM-V 4.7 的 1B 变体推动了多模态大语言模型（MLLMs）的性能-效率帕累托前沿。在指令模式（Instruct mode）下，该 1B 变体在基础能力上继续超越 MiniCPM-V 4.6，并在总分和推理效率方面大幅优于 Qwen3.5 0.8B。
- 🚀 高效架构。基于 LLaVA-UHD v4 中的 ViT 内部早期压缩技术，MiniCPM-V 4.7 在视觉编码器内部尽早压缩视觉 token，从而减少后续编码器层的计算量。它还支持混合 4x/16x 视觉 token 压缩，允许开发者在不同任务中平衡视觉细节与推理成本。
- 💻 广泛的平台和芯片支持。MiniCPM-V 4.7 支持 iOS、Android 和 HarmonyOS，并提供开源的端侧部署支持。它还为瑞芯微（Rockchip）、摩尔线程（Houmo）、Intel、AMD 和 Arm 等芯片平台提供首日（day-0）支持，扩展了移动设备和本地计算设备的部署选项。
- 🛠️ 对开发者友好。MiniCPM-V 4.7 支持包括 SGLang、vLLM、llama.cpp 和 Ollama 在内的推理框架，以及包括 SWIFT 和 LLaMA-Factory 在内的微调框架。它提供 GGUF、BNB、AWQ 和 GPTQ 量化变体，并新增 FP8 量化支持。我们还发布了自回放微调（Self-Replay Fine-Tuning），这是一种帮助开发者以低成本扩展模型能力同时减少现有能力遗忘的微调方法，使进一步定制变得更加容易。"
更进一步：量化金融体系
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