
Hugging Face researchers have released Palette, a method for training small open-weight models to generate presentation decks that adhere strictly to specific design languages. The system fine-tunes a 20B parameter model using synthetic data generated by larger teacher models, splitting the task into editorial planning and geometric coding roles. Evaluated on PresentBench, the model ranked first in two domains by maintaining brand consistency and avoiding common layout errors like overlapping text. The approach demonstrates that specialized small models can outperform generalist frontier models in constrained creative tasks when trained with rigorous synthetic reasoning and automated quality checks.
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© Hugging Face BlogHugging Face launched ToyBlocks, a controlled simulation framework designed to isolate and study the specific mechanics of robot learning. By focusing on a simple tabletop environment with blocks, it allows researchers to automatically generate massive datasets of tasks and demonstrations without manual teleoperation. This setup enables precise testing of how high-level reasoning models like Molmo 2 interact with low-level action controllers like ManiFlow. The real value lies in reproducibility; scientists can now systematically vary training data to understand exactly why a robot fails or succeeds, moving beyond the black-box nature of current robotics benchmarks.
The Open ASR Leaderboard is expanding its evaluation scope by adding private test sets for Egyptian and Nigerian English, addressing a significant gap in global representation. This update moves beyond the traditional Inner Circle accents to include Outer and Expanding Circle varieties, providing a more realistic benchmark for speech recognition models. By partnering with Liva AI for data collection, Hugging Face ensures these evaluations reflect natural, high-context user interactions rather than artificial tasks. This shift forces developers to optimize for diversity, making model performance metrics more trustworthy and globally relevant.
© Hugging Face BlogNVIDIA is opening up its Cosmos 3 world model family with four new 16B parameter checkpoints, shifting from closed research to open-weight simulation. The release targets two critical domains: autonomous driving and robotics. For self-driving cars, the models generate synchronized camera video and LiDAR data, solving the alignment problem between visual appearance and 3D geometry. For robotics, Sim-Bimanual predicts environmental changes from robot actions, enabling closed-loop policy training. This moves world modeling from passive video generation to interactive simulation where agents can act and observe consequences.
This release quietly closes the hardware gap for local inference by adding default support for CUDA 13.5 and ROCm 10.0 alongside existing CUDA 12 builds. AMD GPU users finally get first-class parity with NVIDIA, while Snapdragon mobile chips gain dedicated NPU acceleration paths. Apple Silicon KleidiAI is explicitly disabled here, a notable regression for Mac performance that suggests ongoing tuning issues. The inclusion of OpenVINO and SYCL backends further cements llama.cpp as the universal runtime for inference across nearly every major silicon architecture.
Kimi’s new K3.1-Pro model joins the llama.cpp ecosystem, bringing a distinct architectural twist to local inference. By replacing full-attention MLA layers with DSA (Dynamic Sparse Attention) and linear-attention KDA layers, this release tests how well sparse caching handles hybrid attention patterns. The implementation keeps indexer precision high to avoid quality loss during top-k selection, ensuring the model runs accurately on consumer hardware. This expands the library’s reach into newer Chinese open-weight models that rely on complex memory management.
This release quietly cements llama.cpp as the universal inference runtime by adding default builds for CUDA 13.5 and ROCm 10.0 across Linux and Windows. The inclusion of ROCm 10 means AMD GPU users no longer need custom compilation to access modern acceleration, effectively closing the gap with NVIDIA's ecosystem support. While KleidiAI on Apple Silicon is disabled in this build, the broader platform coverage ensures that local inference remains accessible on virtually any hardware configuration without manual setup.