StarCoder: A State-of-the-Art LLM for Code
Hugging Face released StarCoder, an open-source LLM specifically for code generation, finetuned on a large dataset of GitHub code.
Hugging Face Blog·4 May
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Hugging Face released StarCoder, an open-source LLM specifically for code generation, finetuned on a large dataset of GitHub code.
Hugging Face released a Unity API, enabling developers to integrate Hugging Face models into Unity-based applications.
Hugging Face detailed how to train language models using its Transformers library with TensorFlow on Google's TPUs.
Databricks announced a collaboration with Hugging Face to optimize LLM training and tuning, claiming up to 40% speed improvements.
Hugging Face launched a dedicated Chinese blog and community platform to engage with Chinese AI researchers and developers.
Eugene Yan outlines 9 ML system design patterns, including human-in-the-loop, hard mining, reframing, cascade, data flywheel, and business rules layer.
Hugging Face announced optimization for Transformers on AWS Inferentia2, claiming significant performance and cost improvements for inference.
A small LLM was run on a Raspberry Pico to generate short, creative text formats like headlines and comments, demonstrating local inference.
Hugging Face blog post discusses Substra, an open-source framework for federated learning and privacy-preserving AI.
OpenAI launched a bug bounty program to incentivize researchers to discover and report security vulnerabilities in its models and platforms.
Snorkel AI and Hugging Face partnered to integrate Snorkel Flow's data labeling and programmatic workflow capabilities with Hugging Face models.
OpenAI published a blog post outlining its general approach to AI safety, focusing on responsible development and deployment.
Hugging Face released a guide and code for training LLaMA models using Reinforcement Learning from Human Feedback (RLHF).
Hugging Face published its third 'Ethics and Society' newsletter, focusing on ethical openness in AI development and deployment.
Hugging Face reported BLOOMZ model inference speedup using Habana Gaudi2 accelerators, demonstrating a potential alternative to NVIDIA GPUs.
Hugging Face and Flower collaborated on a blog post demonstrating federated learning for model training, focusing on practical implementation.
OpenAI announced initial support for plugins in ChatGPT, enabling models to access real-time information, run computations, and use third-party services.
Hugging Face and Project Jupyter announced an expanded collaboration to integrate Hugging Face tools directly within Jupyter environments.
LLMs generate biographies to assess memorization and regurgitation patterns.
ARC partners with Anthropic and OpenAI for third-party evaluation of dangerous capabilities in state-of-the-art ML models.
OpenAI research paper assesses labor market impact potential of large language models on various occupations.
Research post defines prompt engineering as an empirical science for steering LLMs without weight updates, requiring experimentation.
Iceland leverages OpenAI's GPT-4 to create language models for Icelandic, addressing low-resource language preservation challenges.
OpenAI's GPT-4 integration with Duolingo improves language tutoring and role-playing conversational experiences.
Khan Academy is piloting GPT-4 to power virtual education. This is a limited program to explore potential applications.
OpenAI's GPT-4 powers Be My Eyes app, offering AI-assisted visual descriptions for blind and low-vision users, expanding accessibility use cases.
Hugging Face blog post discusses using ML in a game to aid survivors, illustrating application of AI in non-traditional contexts.
Hugging Face blog post details how their platform accelerated development of Witty Works' writing assistant, likely a case study.
Hugging Face blog post discusses red-teaming methodologies for LLMs, covering adversarial attacks and safety evaluations.
Fetch, a consumer rewards app, claims 30% development time savings by consolidating AI tools on Hugging Face and AWS for internal MLOps.
Hugging Face and AWS announced a partnership focused on making AI more accessible, including optimized model deployment and training.
OpenAI published a blog post clarifying its approach to model behavior alignment, user customization, and public input in decision-making.
Hugging Face promotes its Inference Endpoints for enterprise model deployment, citing potential cost and operational benefits over self-hosting.
Hugging Face introduced a multi-agent deep reinforcement learning competition system for training and evaluating AI agents in adversarial settings.
Hugging Face blog post discusses the current state and capabilities of Vision-Language Models (VLMs), covering applications and technical foundations.
OpenAI launched ChatGPT Plus, a pilot subscription service for its conversational AI, offering general access and faster response times.
OpenAI released a new classifier to identify AI-generated text, acknowledging its limitations and an accuracy rate of 26% for AI text.
Hugging Face outlined the current state of computer vision models and tooling on its platform, emphasizing open-source contributions.
Hugging Face announced Optimum+ONNX Runtime integration for faster training of their models, aiming for efficiency gains.
OpenAI and Microsoft announced an extension of their existing strategic partnership.
Eugene Yan outlines project mechanisms for effective machine learning, including pilot/copilot, literature and methodology review, and timeboxing.
Hugging Face now supports PaddlePaddle, Baidu's deep learning framework, on its platform, integrating its models and datasets for wider access.
Hugging Face demonstrates image similarity using their datasets and transformers libraries, enabling search and retrieval for visual assets.
OpenAI collaborated with Georgetown and Stanford on a report identifying disinformation risks from LLMs, based on a 2021 workshop and over a year of research.
Research overviews techniques like distillation to optimize large transformer model inference for real-world application at scale, addressing cost and latency.
Hugging Face blog details using AI tools to accelerate game development, creating a farming game in five days.
OpenAI claims GPT-3 delivers fast, nuanced insights from customer feedback, enabling improved product and service understanding.
OpenAI reports fine-tuning GPT-3 to automate and scale video content generation, demonstrating use in a 'done-for-you' video service.
Hugging Face released an introductory blog post on Graph Machine Learning (GML), covering core concepts and use cases.
Hugging Face blog details rapid game development using AI, creating a farming game in five days.
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