Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia
Hugging Face and AWS demonstrate BERT inference acceleration using AWS Inferentia, targeting cost and latency improvements for transformer models.
Hugging Face Blog·16 Mar
Search signals, briefings, company results, benchmarks and glossary terms.
Search signals, briefings, company results, benchmarks and glossary terms.
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Hugging Face and AWS demonstrate BERT inference acceleration using AWS Inferentia, targeting cost and latency improvements for transformer models.
OpenAI released new GPT-3 and Codex models with 'edit' and 'insert' capabilities, allowing modification of existing text.
OpenAI issued a call for expressions of interest to conduct research on the economic impacts of large language models.
OpenAI shares lessons on language model safety and misuse, detailing their approach to preventing harmful applications and ensuring responsible deployment.
OpenAI published research on methods to improve instruction following in large language models, a core capability for enterprise applications.
OpenAI launched new API endpoint for text and code embeddings, enabling semantic search, clustering, topic modeling, and classification tasks.
Hugging Face now officially supports Stable-baselines3, a popular reinforcement learning library, on its Hub for model sharing and deployment.
Enterprise AI leader Eugene Yan details strategies for continuous learning in machine learning, covering technical depth, product thinking, and operationalization.
Hugging Face claims millisecond latency for LLM inference on CPUs using their Infinity service, suggesting performance gains without GPUs.
Hugging Face demonstrates deploying GPT-J 6B for inference on Amazon SageMaker, leveraging Transformers for efficient model serving.
Gradio, a popular open-source library for building machine learning UIs, has joined Hugging Face, integrating its tools deeper into the HF ecosystem.
OpenAI fine-tuned GPT-3 using a web browser for improved factual accuracy on open-ended questions.
OpenAI announced simplified fine-tuning for GPT-3 models via a single command, making customization more accessible for developers.
Eugene Yan and Daliana Liu discussed end-to-end machine learning system building for two hours on The Data Scientist Show podcast.
OpenAI announced an AI Residency program to train talent, offering a full-time, paid position for those without prior AI research experience.
Hugging Face blog details using Optimum for Transformers on Graphcore IPUs, outlining steps for model fine-tuning and deployment.
OpenAI has removed the waitlist for its API, making it immediately available to all developers. OpenAI attributes wider availability to safety progress.
OpenAI reports a new system solves grade school math problems with 55% accuracy, nearly doubling prior GPT-3 performance and approaching human child scores.
Hugging Face blog post discusses the rapid scaling of LLMs, drawing parallels to Moore's Law for computational progress.
Hugging Face released a blog post detailing the process of training a sentence embedding model using one billion training pairs.
Hugging Face promotes 'ML as Code' concept, emphasizing programmatic model development, deployment, and governance over UI-driven approaches.
Hugging Face blog post details using Streamlit for hosting models and datasets on Hugging Face Spaces for public or private sharing.
Hugging Face released several updates including a new inference API, enhanced security features, and expanded fine-tuning capabilities.
OpenAI claims a new method for training models to summarize books using human feedback, improving performance on long, complex tasks.
The article advocates starting with heuristic-based solutions before implementing machine learning to validate problem solving and identify data needs.
Hugging Face and Graphcore partnered to optimize Transformer models for Graphcore's IPU hardware, targeting performance for AI workloads.
Helen Toner, former board member who voted to oust Sam Altman, has rejoined OpenAI's board of directors.
OpenAI released Triton 1.0, an open-source Python-like programming language for writing efficient GPU code for neural networks without CUDA expertise.
Hugging Face proposes collaborative, decentralized training of large language models over the internet, distributing compute across multiple parties.
spaCy integrated its natural language processing library with the Hugging Face Hub for easier model discovery, sharing, and deployment.
Hugging Face announced easier deployment of its models on Amazon SageMaker, streamlining access to managed inference infrastructure for open-source models.
OpenAI published research on evaluating large language models for code generation, focusing on benchmarks for correctness and safety.
Hugging Face is integrating Sentence Transformers as a core feature on its Hub, simplifying access and management of these embedding models.
OpenAI research suggests fine-tuning with small, curated datasets improves LLM alignment to specific behavioral values.
Hugging Face details practical few-shot learning with GPT-Neo via their Accelerated Inference API, showcasing technique, not new model capability.
Hugging Face released Gradio 2.0, an open-source library for building and sharing ML model UIs, now with improved component mixing.
OpenAI Scholars 2021 class completed its six-month mentorship program and produced open-source research projects.
Former Congressman Will Hurd joins OpenAI's board of directors, adding public policy and national security experience to the board.
Eugene Yan outlines the process of applying machine learning in enterprise settings to achieve impact, moving beyond theoretical knowledge.
Eugene Yan discussed life lessons from machine learning on the Talk Python podcast, covering philosophical parallels between ML and life.
OpenAI reports over 300 applications are leveraging GPT-3 via API for search, conversation, and text completion.
Amazon SageMaker now integrates Hugging Face's open-source models and tools, offering new capabilities for model training, fine-tuning, and deployment.
Enterprise AI leader Eugene Yan discusses problem selection in data science, highlighting trade-offs between short-term wins and long-term impact.
Hugging Face provided a guide on fine-tuning Wav2Vec2 for English Automatic Speech Recognition using their Transformers library.
Hugging Face blog post from Feb. 2021 discussing the emergence of long-range Transformer architectures.
Eugene Yan outlines best practices for creating design documents for machine learning systems, covering methodology, implementation, and review.
OpenAI discovered 'multimodal neurons' in CLIP that respond consistently to concepts across literal, symbolic, and conceptual representations.
Hugging Face blog post discusses foundational considerations for building neural networks, emphasizing practical aspects over advanced theory.
Top teams in a 36-hour data hackathon succeeded not through advanced ML, but by focusing on data preprocessing, feature engineering, and robust pipelines.
Hugging Face announced support for PyTorch/XLA on Google TPUs, offering an alternative for training and fine-tuning large models.
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