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Profiling in PyTorch (Part 3): Attention is all you profile
Hugging Face has released the third part of its technical guide on profiling performance within the PyTorch framework, focusing on attention mechanisms.
Hugging Face Blog
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Data for Agents
Hugging Face has published an analysis focusing on the role and preparation of training data specifically optimized for artificial intelligence agents.
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Native-speed vLLM transformers modeling backend
Hugging Face has introduced a native-speed vLLM backend for transformers to optimize and accelerate large language model serving.
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From Hugging Face to Amazon SageMaker Studio in one click
Hugging Face has launched a one-click integration with Amazon SageMaker Studio to streamline the deployment and training of machine learning models.
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Hugging Face Models on Foundry Managed Compute
Hugging Face has integrated its model repository with Foundry Managed Compute to provide developers with scalable, managed infrastructure for running AI models.
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Run AI workloads on any cloud, store on Hugging Face: zero-egress storage with SkyPilot
Hugging Face has partnered with SkyPilot to enable zero-egress storage, allowing developers to run artificial intelligence workloads across multiple cloud providers while keeping data stored on Hugging Face.
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LeRobot v0.6.0: Imagine, Evaluate, Improve
Hugging Face has released version 0.6.0 of LeRobot, its open-source library designed for training and evaluating robotic learning models.
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PRX Part 4: Our Data Strategy
Hugging Face has published the fourth installment of its PRX series, detailing the data strategy used for training its models.
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🤗 Kernels: Major Updates
Hugging Face has announced major updates to its Kernels platform to improve user workflows.
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Hugging Face and Cerebras bring Gemma 4 to real-time voice AI
Hugging Face and Cerebras have partnered to optimize Google's Gemma 4 model for low-latency, real-time voice artificial intelligence applications.
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ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration
A new benchmark called ScarfBench has been introduced to evaluate the performance of artificial intelligence agents in migrating enterprise Java frameworks.
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Why Specialization Is Inevitable
Hugging Face discusses the factors driving the inevitable shift toward specialized artificial intelligence models rather than general-purpose ones.
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Featuring Every Eval Ever Results on Hugging Face Model Pages
Hugging Face has integrated comprehensive evaluation results directly onto its model pages to improve transparency and model comparison for developers.
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DiScoFormer: One transformer for density and score, across distributions
Researchers have introduced DiScoFormer, a unified transformer architecture capable of modeling both density and score functions across various distributions.
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Run a vLLM Server on HF Jobs in One Command
Hugging Face has introduced a single-command solution to run a vLLM server on Hugging Face Jobs.
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Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel
Hugging Face has detailed how to accelerate transformer model fine-tuning using NVIDIA NeMo AutoModel.
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Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World
Hugging Face has launched the FFASR Leaderboard to benchmark automatic speech recognition performance in real-world scenarios.
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Shipping huggingface_hub every week with AI, open tools, and a human in the loop
Hugging Face detailed its weekly release process for the huggingface_hub library, which utilizes AI assistance alongside human oversight.
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Experimenting with the proposed Cross-Origin Storage API in Transformers.js
Developers are testing a proposed Cross-Origin Storage API within Transformers.js to improve model sharing across different web domains.
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PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters
The PP-OCRv6 text recognition model, supporting fifty languages with parameter sizes ranging from 1.5 million to 34.5 million, is now available on Hugging Face.
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We got local models to triage the OpenClaw repo for FREE!*
Hugging Face demonstrated how local open-source language models can be used to triage issues in the OpenClaw repository without incurring API costs.
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MosaicLeaks: Can your research agent keep a secret?
A new study on Hugging Face examines the security risks of research agents inadvertently leaking sensitive information.
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Beyond LoRA: Can you beat the most popular fine-tuning technique?
Hugging Face explored alternative fine-tuning methodologies that aim to surpass the performance of the widely used Low-Rank Adaptation technique.
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Is it agentic enough? Benchmarking open models on your own tooling
Hugging Face has published a guide on benchmarking open-source models to evaluate their agentic capabilities using custom tools.
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