Hugging Face
AI Platform
Open-source ML platform, model hub, Transformers library.
Watched repos
Recent activity
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Diffusers now supports loading Nunchaku 4-bit diffusion inference checkpoints without requiring a separate inference engine, leveraging the Hugging Face kernels package to download necessary CUDA kernels from the Hub. This enables faster and lower memory usage inference, reducing the VRAM requirements to around 12 GB for the 1024x1024 image size, compared to 24 GB for BF16 precision. AI summary
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Simulation for physical AI systems relies on generating large amounts of physically grounded data, which is challenging to collect in the real world due to safety, cost, and practicality concerns. Simulation engines like MuJoCo, Isaac Sim, and others can generate photorealistic data using GPU parallelism, enabling developers to train reinforcement learning policies and test policies against rare scenarios. The choice of simulation engine depends on factors such as scalability, sensor support, 3D asset formats, and environmental fidelity required for the specific use case. AI summary
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Grabette is an open-source system for recording robot-manipulation data, allowing users to capture demonstrations with a handheld gripper and a camera, and then process the data into a robot-ready dataset. The system consists of a handheld device (Grabette) and a motorized gripper (Gripette) that work together to record and execute tasks, with the goal of creating a large, open, and collaborative dataset for robot learning. Users can build Grabette using standard components and a Raspberry Pi, and then use the system to record demonstrations and generate datasets for training machine learning models. AI summary
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Researchers found that DharmaOCR outperformed newer OCR models (Mistral OCR4 and Unlimited-OCR) on Brazilian Portuguese due to domain specialization and targeted training, where all model parameters were dedicated to the specific task of recognizing Brazilian Portuguese text. This approach enables the model to concentrate its resources on the target language, resulting in a structural advantage over multilingual models. The advantage of specialization does not depend on the model's architecture or training procedure, but rather on the direction of its resources. AI summary
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Hugging Face detected and responded to an AI-driven intrusion into its production infrastructure, which was driven by an autonomous AI agent system and resulted in unauthorized access to internal datasets and credentials. The attack exploited vulnerabilities in the data-processing pipeline, and the company fixed the root vulnerability, eradicated the attacker's foothold, and implemented additional guardrails and stricter admission controls. The incident highlights the need for defenders to treat the data and model surface as a first-class attack surface and use AI on defense to keep pace. AI summary
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Building Shippy, a maritime AI agent, taught developers about the importance of reliability and determinism in high-stakes decision-making domains, where a wrong answer can have real-world consequences. Shippy's architecture emphasizes a "soul," "skills," and "config" approach, where the system prompt, skills, and configuration are versioned and easily revisable. The agent uses deterministic tools, such as a purpose-built CLI, to interact with external APIs and minimize the risk of nondeterministic errors. AI summary
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Model routing, a simple concept, can be surprisingly complex due to factors like caching, task complexity, and latency, which can't be reduced to a simple classification problem. A traditional router that only considers pricing and task difficulty may not optimize the best performance, leading to suboptimal results. To address this, the authors shifted their approach from a classification problem to an optimization problem, considering multiple dimensions like cost, quality, and latency simultaneously. This allows for a more nuanced and flexible routing strategy that can adapt to various serving conditions and infrastructure factors. AI summary
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A new benchmark, Real World VoiceEQ, has been introduced to measure the human quality of voice AI, evaluating voice systems' ability to recognize, produce, and respond to acoustic information such as tone, emotion, and speaker identity. The benchmark assesses over 40 leading voice models across 15+ key evaluation dimensions and 60 metrics, including Automatic Speech Recognition, Text-to-Speech, and Speech Understanding. This provides a more comprehensive evaluation of voice AI's performance in real-world conversations. AI summary
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Thinking Machines' Inkling is a large, multimodal LLM (~1T params) that can accept image, text, and audio inputs, trained on 45 trillion tokens of data, and is focused on reasoning across modalities. It uses a decoder-only architecture with a Mixture-of-Experts (MoE) model and supports 1M context window, featuring relative attention, hybrid attention, and short convolution. Inkling is supported in major inference engines like SGLang and vLLM, with day-0 transformers support. AI summary
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PyTorch's naive attention implementation involves a series of operations, including matmuls, multiplications, and a softmax function, which can be profiled to understand their performance characteristics. By analyzing the profiler trace, it's possible to identify the individual kernels launched for each operation, including a memory copy operation that can be optimized by using in-place masking. AI summary
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The transformers library now offers a native-speed vLLM transformers modeling backend, allowing model authors to automatically leverage their transformers implementations for ultra-fast vLLM inference. This integration enables model authors to run transformers models (LLMs and VLMs alike) inside vLLM without having to port anything, and achieves native throughput on various models, including 4B dense, 32B dense, and 235B-parameter FP8 Mixture-of-Experts models. AI summary
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