#开源生态

@OpenBMB dropped MiniCPM5-2B three days ago and it is the best open model under 4B right now. 2.5B params, Apache 2.0, 128K context, and it beats Qwen3.5 4B with 44 percent fewer parameters. Number 1 open weights model under 4B on the Artificial Analysis Intelligence Index, level with Qwen3.5 9B at roughly a quarter of the size. Where it really stands out is code and agents. On OpenBMB's own table it averages 53.9 across 34 benchmarks against 51.1 for the best 4B model. LiveCodeBench 69.1 vs 56.4 for Qwen3.5 4B. SWE-bench Verified 46.4 vs 33.6. Tool use at 97 on tau2 Telecom. And it is one checkpoint that can think or answer fast, you pick per request. The trick is how they built it. They RL trained 16 specialist models, 5 of them agentic, then distilled all of them back into this one 2B model. Full training data is public too. Just ran it on the @RunAnywhereAI Android app btw, llama.cpp, GGUF pulled straight from Hugging Face inside the app, nothing leaves the phone.

Jacob is correct here—we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.

🤗 Novita now supports Ling-3.0-flash-VL on @huggingface. 🎁 Free for 14 days. • 124B total parameters · 5.5B active parameters per token • Native image and video understanding • Built for multimodal reasoning and agentic workflows

inclusionAI's Ling-3.0-flash-VL brings vision to agent workflows, with BF16 and FP8 weights.📜 MIT. 🤖 https://modelscope.ai/models/inclusionAI/Ling-3.0-flash-VL 🤖 https://modelscope.ai/models/inclusionAI/Ling-3.0-flash-VL-fp8 🏆 Scores 42 on the Artificial Analysis Intelligence Index v4.1.1, up 4 points from Ling-3.0-flash. 👁️ Read charts and documents, count objects, and reason with visual evidence. Turn web and software interfaces into action sequences. 🎬 Understand images and videos. VideoRoPE tracks space and time for event localization, long-video Q&A, and video editing workflows. 🧠 124B total parameters, just 5.5B active per token. A hybrid KDA + Gated MLA backbone supports context up to 1M tokens. 📦 Choose BF16 or FP8 for lower weight memory. Deployment recipes cover SGLang and vLLM.

Meet Ling-3.0-flash-VL (124B/5.5B MoE) from @AntLingAGI, with native image + video understanding and a 1M context window. Day-0 support is live in SGLang! > Native multimodal: ViT encoder + MLP projector brings vision into the full loop of understanding, reasoning, acting, and verification > VideoRoPE: encodes space + time for event localization, long-video QA, and clip editing > 42-layer hybrid backbone: KDA + Gated MLA at 5:1 for efficient 1M-token long context Run Ling-3.0-flash-VL with SGLang!

Today marks a major step for Mistral: we’re announcing a €3B Series D, the largest equity round ever raised by a European tech company, just three years after launch.

🚀 Meet MiniCPM5-2B, a 2B-parameter language model bringing high intelligence density to the edge, now open source! It ranks #1 among open-source models under 4B parameters on the @ArtificialAnlys Intelligence Index, with a score of 23. It also scores 20 on the Agentic Index, bringing an early form of general-purpose agent capability to the edge. Across 34 benchmarks, MiniCPM5-2B achieves an average score of 53.9, covering coding, math, long-context understanding, tool use, and agentic tasks. And this release goes beyond the model itself. We’re opening up the data, training recipes, and RL stack behind MiniCPM5-2B. 🤗 Hugging Face: http://huggingface.co/openbmb/MiniCPM5-2B 💻 GitHub: http://github.com/OpenBMB/MiniCPM Modelscope: http://modelscope.cn/models/OpenBMB/MiniCPM5-2B Web: https://openbmb.cn/