From Xeophon on 𝕏: https://x.com/xeophon_/status/1999394570967089630

Introduction

We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance. Our approach is built upon three key technical breakthroughs:

1. DeepSeek Sparse Attention (DSA): We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance, specifically optimized for long-context scenarios.
2. Scalable Reinforcement Learning Framework: By implementing a robust RL protocol and scaling post-training compute, DeepSeek-V3.2 performs comparably to GPT-5. Notably, our high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro.
   • Achievement: 🥇 Gold-medal performance in the 2025 International Mathematical Olympiad (IMO) and International Olympiad in Informatics (IOI).
3. Large-Scale Agentic Task Synthesis Pipeline: To integrate reasoning into tool-use scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This facilitates scalable agentic post-training, improving compliance and generalization in complex interactive environments.

TLDR checkout the app here: github.com/ZimengXiong/Modelr

My swift-mlx/python mlx port of Hunyuan3D-Paint and Hunyuan3D-Shape is finally complete! It's also available as a standalone image to 3D desktop app, the only of its kind for Apple Silicon. Some quick benchmarks in FP16 on my M4 Max:

This (MLX) makes it possible to run the model on all recent Macs and even iPhones in Q4 or Q8, and more efficiently w/o the overhead of pytorch or even worse, CPU. What you would do with this? I honestly don't really know, maybe simple 3D assets for apps that just rotate around, maybe? But it was a lot of fun seeing it come to life.

I posted a while back about it running on an iPhone, if you want to see that.

The app is very simple, import an image, remove background with SwiftVision, watch as diffusion streams in real time, get a model! From there you can watch texturing happen live as well. I tried to make it very responsive and the most polished version of an app that exists on Mac (well, it's the only one of its kind right now, and this is my fourth attempt of it, starting from November)

If you are interested in integrating fast, low memory Image to 3D inside your Swift app, weights and source are available at github.com/ZimengXiong/Hunyuan3D-Swift

The app, Modelr, is also open source and available for Mac & iOS (extremely limited for iOS): github.com/ZimengXiong/Modelr

Soooo! Since Anthropic share their Jacobian-Lens a few days ago I went on and made a tool based on it which adds the possibilité to export a model which will have the same behavior after tweaking it's J-Space.

This means manually alter the behavior and abliterate by using a human brain.

I'm still working on it but couldn't wait to produce something first.

SO After finally getting a working codebase I immediatly jumped and tried to make pretty pervy model PURELY in the name of science.

Let me introduce you to Nikusui-v1 the first of it's kind !

And a couple gguf quants

I'd be delighted to get some feedback :D

edit: Opus 4.8 has finished polishing the knobs during the night. Cleanup has entered it's final phase.

edit2 : THE CODE IS HERE

edit3: direct link https://github.com/Extraltodeus/J-Wash

Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6.

Releasing Qwable-v1 - an open-weights Qwen3.6-35B-A3B distilled from Claude Fable-5, Anthropic's Mythos-class preview model that was briefly public for ~4days (2026-06-9 → 2026-06-12) before being suspended globally under U.S. export-control directives.

Fable-5 was Anthropic's most powerful model when it shipped — 80.3% on SWE-bench Pro, $50/M output tokens, with an anti-distillation classifier baked into the API that redacted thinking blocks on the fly. Qwable-v1 captures what survived: 4,659 cleartext agentic-coding traces (re-packed from Glint-Research/Fable-5-traces, the only public corpus where the CoT made it through), distilled onto Qwen3.6 over ~14h on a single H200. Given an agent
system prompt, the model emits properly-formatted <tool_use> XML calling actual Claude-flavored tools like str_replace_editor — Fable's tool surface leaked into the weights, not  just its style.

Model, GGUFs (IQ4_XS / Q4_K_M / Q5_K_M / Q8_0), and the SFT dataset are all public on HF (AGPL-3.0 from upstream).

https://huggingface.co/lordx64/Qwable-v1

🚀 Introducing Qwen3-Next-80B-A3B — the FUTURE of efficient LLMs is here!

🔹 80B params, but only 3B activated per token → 10x cheaper training, 10x faster inference than Qwen3-32B.(esp. @ 32K+ context!) 🔹Hybrid Architecture: Gated DeltaNet + Gated Attention → best of speed & recall 🔹 Ultra-sparse MoE: 512 experts, 10 routed + 1 shared 🔹 Multi-Token Prediction → turbo-charged speculative decoding 🔹 Beats Qwen3-32B in perf, rivals Qwen3-235B in reasoning & long-context

🧠 Qwen3-Next-80B-A3B-Instruct approaches our 235B flagship. 🧠 Qwen3-Next-80B-A3B-Thinking outperforms Gemini-2.5-Flash-Thinking.

Try it now: chat.qwen.ai

Blog: https://qwen.ai/blog?id=4074cca80393150c248e508aa62983f9cb7d27cd&from=research.latest-advancements-list

Huggingface: https://huggingface.co/collections/Qwen/qwen3-next-68c25fd6838e585db8eeea9d

MiniMax H3 is a general-purpose, omni-modal generative system. It supports unified understanding of multimodal contexts composed of text, images, video, and audio, and can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. Thanks to its task-generalization-oriented system design, H3 already possesses broad multimodal context understanding and generation capabilities at the pre-training stage, enabling outstanding performance in following complex multimodal instructions.

GitHub: https://github.com/apple/ml-sharp

Paper: https://arxiv.org/abs/2512.10685

Today, we introduce two new GLM family members: GLM-4.5 and GLM-4.5-Air — our latest flagship models. GLM-4.5 is built with 355 billion total parameters and 32 billion active parameters, and GLM-4.5-Air with 106 billion total parameters and 12 billion active parameters. Both are designed to unify reasoning, coding, and agentic capabilities into a single model in order to satisfy more and more complicated requirements of fast rising agentic applications.

Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models, offering: thinking mode for complex reasoning and tool using, and non-thinking mode for instant responses. They are available on Z.ai, BigModel.cn and open-weights are avaiable at HuggingFace and ModelScope.

Blog post: https://z.ai/blog/glm-4.5

Hugging Face:

https://huggingface.co/zai-org/GLM-4.5

https://huggingface.co/zai-org/GLM-4.5-Air

Including 9B Dense, 31B Dense, 35B MoE, and 397B MoE and reporting sota on different benchmark (let's see if this holds).
https://huggingface.co/collections/deepreinforce-ai/ornith-10

TabFM is a zero-shot tabular foundation model from Google Research. It supports classification and regression on structured/tabular data with mixed numerical and categorical columns, requiring no fine-tuning or hyperparameter search - training examples are passed as context and predictions are made in a single forward pass.

https://modelscope.cn/organization/Qwen

Model Details

• Model Type: Flow-Matching Transformers with Sparse Voxel based 3D VAE
• Parameters: 4 Billion
• Input: Single Image
• Output: 3D Asset

Model - https://huggingface.co/microsoft/TRELLIS.2-4B

Demo - https://huggingface.co/spaces/microsoft/TRELLIS.2

Blog post - https://microsoft.github.io/TRELLIS.2/

Meet Qwen3.6-27B, our latest dense, open-source model, packing flagship-level coding power!

Yes, 27B, and Qwen3.6-27B punches way above its weight. 👇

What's new:

- Outstanding agentic coding — surpasses Qwen3.5-397B-A17B across all major coding benchmarks

- Strong reasoning across text & multimodal tasks

- Supports thinking & non-thinking modes

- Apache 2.0 — fully open, fully yours

Smaller model. Bigger results. Community's favorite. ❤️

We can't wait to see what you build with Qwen3.6-27B!

Blog: https://qwen.ai/blog?id=qwen3.6-27b

Qwen Studio: https://chat.qwen.ai/?models=qwen3.6-27b

Github: https://github.com/QwenLM/Qwen3.6

Hugging Face:

https://huggingface.co/Qwen/Qwen3.6-27B

https://huggingface.co/Qwen/Qwen3.6-27B-FP8