面壁智能 OpenBMB 推出 Self-Replay Fine-Tuning 并开放 MiniCPM-V 4.7 全栈部署
面壁智能 OpenBMB 发布 Self-Replay Fine-Tuning，称其为一种低成本定制模型同时保留通用性能的方法，以缓解微调新任务带来的通用能力损失。
Customize with Ease. Deploy Across Devices.
Fine-tuning for new tasks can come at the cost of a model's general capabilities. To address this, we introduce Self-Replay Fine-Tuning—a simple, low-cost way to customize models while preserving its general performance.
When it's time to deploy, MiniCPM-V 4.7 scales from mobile devices to PCs and edge systems. Day-0 hardware support covers Apple, Intel, AMD, Arm and Rockchip, with additional platforms supported through FlagOS, including Huawei, Hygon, Moore Threads and Kunlunxin.
Build with familiar tools throughout your workflow:
🔹 Inference: SGLang, vLLM, llama.cpp, mlx-vlm and Ollama
🔹 Fine-tuning: SWIFT and LLaMA-Factory
🔹 Quantization: GGUF, BNB, AWQ, GPTQ and FP8
The full stack is open source:
Give MiniCPM-V 4.7 a try, and let us know how it performs on your hardware!
🔗 GitHub: github.com/OpenBMB/MiniCPM-V
🤗 Hugging Face: huggingface.co/collections/o…
🌐 ModelScope: modelscope.cn/collections/Op…
🧪 Web Demo: huggingface.co/spaces/openbm…
📱 Apps: github.com/OpenBMB/MiniCPM-V…
Self-Replay Fine-Tuning: openbmb-dev.modelbest.co/en/…
来源：OpenBMB · x.com