Hugging Face's TensorFlow Philosophy
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#未经AI处理
Hugging Face Blog
Hugging Face Blog Deploying 🤗 ViT on Kubernetes with TF Serving
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OpenAI News New and improved content moderation tooling
【RSS 原文 · 未经 AI 筛选或翻译】We are introducing a new and improved content moderation tool. The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers.
Hugging Face Blog Train and Fine-Tune Sentence Transformers Models
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Hugging Face Blog Proximal Policy Optimization (PPO)
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Hugging Face Blog Introducing the Private Hub: A New Way to Build With Machine Learning
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Hugging Face Blog Nyströmformer: Approximating self-attention in linear time and memory via the Nyström method
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Hugging Face Blog Comments on U.S. National AI Research Resource Interim Report
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OpenAI News Efficient training of language models to fill in the middle
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Hugging Face Blog Introducing new audio and vision documentation in 🤗 Datasets
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Hugging Face Blog Faster Text Generation with TensorFlow and XLA
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OpenAI News A hazard analysis framework for code synthesis large language models
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Hugging Face Blog Deploying TensorFlow Vision Models in Hugging Face with TF Serving
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Hugging Face Blog Advantage Actor Critic (A2C)
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OpenAI News DALL·E now available in beta
【RSS 原文 · 未经 AI 筛选或翻译】We’ll invite 1 million people from our waitlist over the coming weeks. Users can create with DALL·E using free credits that refill every month, and buy additional credits in 115-generation increments for $15.
OpenAI News Reducing bias and improving safety in DALL·E 2
【RSS 原文 · 未经 AI 筛选或翻译】Today, we are implementing a new technique so that DALL·E generates images of people that more accurately reflect the diversity of the world’s population.
Hugging Face Blog How to train your model dynamically using adversarial data
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OpenAI News DALL·E 2: Extending creativity
【RSS 原文 · 未经 AI 筛选或翻译】As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows. The artists in our early access group have helped us discover new uses for DALL·E and have served as key voices as we’ve made decisions about DALL·E’s features.
Hugging Face Blog The Technology Behind BLOOM Training
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Hugging Face Blog Building a Playlist Generator with Sentence Transformers
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Hugging Face Blog Introducing The World's Largest Open Multilingual Language Model: BLOOM
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Hugging Face Blog Getting Started with Sentiment Analysis on Twitter
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Hugging Face Blog Policy Gradient with PyTorch
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Hugging Face Blog Liftoff! How to get started with your first ML project 🚀
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OpenAI News DALL·E 2 pre-training mitigations
【RSS 原文 · 未经 AI 筛选或翻译】In order to share the magic of DALL·E 2 with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails in place to prevent generated images from violating our content policy.
Hugging Face Blog Accelerate Large Model Training using DeepSpeed
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Hugging Face Blog Announcing Evaluation on the Hub
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OpenAI News Learning to play Minecraft with Video PreTraining
【RSS 原文 · 未经 AI 筛选或翻译】We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Our model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents.
Hugging Face Blog Getting Started With Embeddings
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Hugging Face Blog Convert Transformers to ONNX with Hugging Face Optimum
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OpenAI News Evolution through large models
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Hugging Face Blog Intel and Hugging Face Partner to Democratize Machine Learning Hardware Acceleration
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Hugging Face Blog Director of Machine Learning Insights [Part 3: Finance Edition]
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OpenAI News AI-written critiques help humans notice flaws
【RSS 原文 · 未经 AI 筛选或翻译】We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques. Larger models are better at self-critiquing, with scale improving critique-writing more than summary-writing. This shows promise for using AI systems to assist human supervision of AI systems on difficult tasks.
OpenAI News Techniques for training large neural networks
【RSS 原文 · 未经 AI 筛选或翻译】Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation.
Hugging Face Blog Deep Q-Learning with Space Invaders
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Hugging Face Blog The Annotated Diffusion Model
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OpenAI News Best practices for deploying language models
【RSS 原文 · 未经 AI 筛选或翻译】Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.
OpenAI News Teaching models to express their uncertainty in words
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Hugging Face Blog Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers
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