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News : MiniMax-M3.1-Flash: Run Locally !

Run MiniMax-M3.1-Flash locally with Unsloth Dynamic GGUFs. MiniMax-M3.1-Flash is MiniMax’s new 428B MoE multimodal model, with 23B active parameters and 1M context, built for coding and agentic use. Learn how to run MiniMax-M3.1-Flash via Unsloth GGUFs locally with Unsloth Desktop or llama.cpp. Our 129.4GB 1-bit achieves ~89.9% top-1 accuracy; 143.9GB 2-bit reaches 91.5%, and 249.4 GB 4-bit UD-Q4_K_XL 98.0%. On coding, Q2 reaches 91.2% and Q4 97.6%. We weighted quant performance across seven categories: coding, math, chat, tools, multilingual, reasoning and agentic use. Thank you MiniMax for day-zero access.

See this news on the unsloth.ai official webpage.

MiniMax-M3.1-Flash is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to 1M tokens. Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding.

See the huggingface model webpage.

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News : Bringing foundational models to the front lines of TB screening.

Tuberculosis remains one of the world's deadliest infectious diseases, claiming an estimated 1.23 million lives each year. Despite decades of global investment, too many cases go undetected in the communities that carry the highest burden. Over the past five years, we’ve worked to change that trajectory, moving from early research benchmarks toward open foundation models that could be tuned and deployed at the point of care.

See the official website.

News : Hugging Face has released open-source WebGPU kernels for local AI workloads.

Hugging Face has released (opens in a new tab) an initial collection of 207 open-source WebGPU kernels for local AI workloads, accompanied by a JavaScript library that downloads and runs them directly from the Hugging Face Hub. Announced by Nico Martin (opens in a new tab) and Joshua Lochner (opens in a new tab), the collection covers the low-level operations that browser inference runtimes repeatedly need: matrix multiplication, normalization, convolution, attention primitives, quantization, broadcasting and tensor layout transformations. Everything is Apache 2.0 licensed. This is foundational work rather than another model demo. Faster browser inference ultimately depends on faster individual operations, and WebGPU portability alone does not guarantee good performance across every device.

See the official website.

News : 3D is the language of the world. And ours. Introducing Tripo’s new visual identity.