✷ The archive · 11 labs · 3,432 dispatches
The labs
No more opening a dozen official blogs every morning. First-hand releases from OpenAI, Anthropic, DeepMind and the rest, each with its substance pulled out.
Hugging Face Ethics and Society Newsletter #4: Bias in Text-to-Image Models
Hugging Face outlines the complex sources of bias in text-to-image models and proposes a multi-pronged approach using exploration tools, red-teaming, and documentation to detect and mitigate these biases.
Hugging Face Open LLM Leaderboard MMLU Evaluation Analysis
Hugging Face explains how different implementation details in the MMLU benchmark lead to significantly different performance scores and rankings for the same LLMs.
Panel Integration with Hugging Face Spaces
Hugging Face has integrated a Panel template into Hugging Face Spaces, allowing users to build and deploy Python-based dashboards and complex applications effortlessly.
Hugging Face Response to the U.S. NTIA Request for Comment on AI Accountability
On June 12, 2023, Hugging Face submitted a response to the U.S. NTIA’s request for comment on AI accountability, recommending that accountability mechanisms cover the full ML lifecycle, combine internal documentation with external transparency, and involve a broad range of stakeholders.
Fine-Tune MMS Adapter Models for Low-Resource ASR
Hugging Face announced that fine‑tuning adapter layers on Meta AI's Massive Multilingual Speech (MMS) models yields state‑of‑the‑art word error rates for low‑resource languages after only minutes of training, while being more memory‑efficient than full model fine‑tuning.
Autoformer – Transformers are effective for time series forecasting
Hugging Face announced that the Autoformer model is now available in the 🤗 Transformers library, showing that Transformer‑based models outperform simple linear baselines like DLinear on time‑series forecasting benchmarks.
Hugging Face Content Guidelines and Policy Update
Hugging Face has updated its Content Policy to address the unique challenges of moderating machine learning artifacts and to establish consent as a core value for protecting user rights and privacy.
Deploying Livebook Notebooks as Apps to Hugging Face Spaces
Hugging Face now supports the deployment of Livebook notebooks as interactive applications to Hugging Face Spaces, enabling Elixir-based machine learning apps to be shared easily.
Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac
Hugging Face and Apple have introduced 6-bit palettization and updated attention layer optimizations in Core ML to significantly reduce memory usage and increase inference speed for Stable Diffusion on Apple devices.
OpenAI Function Calling and API Updates
OpenAI introduced function calling for gpt-4-0613 and gpt-3.5-turbo-0613, enabling models to output structured JSON for external tool and API integration.
Hugging Face and AMD Partnership for Model Acceleration
Hugging Face and AMD have partnered to optimize state-of-the-art transformer models for AMD CPUs, GPUs, and AI accelerators to improve cost-performance standards for training and inference.
Anthropic AI Accountability Recommendations for NTIA
Anthropic has proposed a comprehensive framework for AI accountability to the NTIA, focusing on standardized evaluations, risk-responsive assessments, and pre-registration of large training runs.
The Hugging Face Hub for Galleries, Libraries, Archives and Museums
Hugging Face published a guide showing how galleries, libraries, archives, and museums can use its Hub to share models, datasets, and demos, lowering barriers to machine learning in the GLAM sector.
Can Foundation Models Label Data Like Humans? Hugging Face Analysis
Hugging Face investigated whether GPT-4 can reliably replace human labelers for LLM preference evaluation, finding significant positional and verbosity biases that limit its accuracy as a proxy for human judgment.
Hugging Face Hub and DuckDB Integration for Dataset Analysis
Hugging Face now allows users to run SQL queries via DuckDB on over 50,000 public datasets automatically converted to Parquet format on the Hub.
fastText Integration with Hugging Face Hub
Hugging Face now hosts official mirrors of fastText word vectors for 157 languages and a language identification model, simplifying model access and deployment.
Falcon models release and Hugging Face ecosystem integration
Hugging Face announced the Falcon family of open Apache‑2.0 language models, highlighting Falcon‑40B’s top ranking on the Open LLM Leaderboard and showing how to run, quantize, and fine‑tune them with Hugging Face tools.
AI Speech Recognition in Unity
Hugging Face provides a guide and API for implementing state-of-the-art Automatic Speech Recognition (ASR) within Unity games to enable voice commands and NPC interactions.
OpenAI Cybersecurity Grant Program
OpenAI has launched a $1M Cybersecurity Grant Program to empower defenders and quantify AI's role in cybersecurity, later evolving to include $10M in API credits and Trusted Access for Cyber with the release of GPT-5.3-Codex.
Hugging Face Open Source AI Game Jam Announcement
Hugging Face is hosting the Open Source AI Game Jam from July 7-9, 2023, to encourage game developers to integrate open-source generative AI tools into their development workflows and game features.
OpenAI Improving Mathematical Reasoning with Process Supervision
OpenAI has developed a method to improve mathematical problem solving by rewarding each correct step of reasoning (process supervision) rather than just the final answer (outcome supervision), resulting in state-of-the-art performance and better AI alignment.
BERTopic Integration with the Hugging Face Hub
The Hugging Face blog post announcing BERTopic integration with the Hub does not provide readable details beyond its title.
Hugging Face LLM Inference Container for Amazon SageMaker
Hugging Face has introduced a purpose-built LLM Inference Container (DLC) for Amazon SageMaker, powered by Text Generation Inference (TGI) to enable high-performance deployment of open-source Large Language Models.
OpenAI Democratic Inputs to AI Program
OpenAI launched a grant program to fund ten $100,000 experiments aimed at developing democratic processes for deciding the rules AI systems should follow.
Optimizing Stable Diffusion for Intel CPUs with NNCF and 🤗 Optimum
Hugging Face announced a workflow using NNCF and Optimum to quantize Stable Diffusion and apply Token Merging, achieving up to 5.1x inference speedup and a 4x reduction in model footprint on Intel CPUs.
Hugging Face Model Catalog on Azure Machine Learning
Hugging Face and Microsoft have launched a native integration of the Hugging Face Hub Model Catalog within Azure Machine Learning Studio to simplify the deployment of open-source models on secure Azure infrastructure.
Hugging Face 4-bit Quantization and QLoRA Release
Hugging Face announced 4-bit quantization support via bitsandbytes and the QLoRA method, enabling inference and adapter fine‑tuning of large language models on consumer GPUs.
Anthropic Interpretability Dreams Research Vision
Anthropic outlines its long-term vision for mechanistic interpretability, focusing on resolving the challenge of superposition to enable the analysis of massive neural networks.
Anthropic Circuits Updates May 2023
Anthropic's May 2023 interpretability updates cover research into multiagent system failures, worker retraining program evidence, and a research version of Claude's progress on the Riemann zeta function.
Instruction-tuning Stable Diffusion with InstructPix2Pix
Hugging Face explores extending InstructPix2Pix to teach Stable Diffusion to follow specific image translation and low-level processing instructions through a multi-task instruction-tuning approach.
Hugging Face Safetensors Security Audit and Default Adoption
Hugging Face, EleutherAI, and Stability AI are transitioning to safetensors as the default model saving format following a security audit by Trail of Bits that confirmed no critical flaws leading to arbitrary code execution.
Hugging Face and IBM Partnership for watsonx.ai
Hugging Face and IBM have partnered to integrate Hugging Face open-source libraries and models into watsonx.ai, an enterprise AI studio for training, tuning, and deploying foundation models.
Anthropic raises $450M Series C to scale reliable AI products
Anthropic announced a $450 million Series C round led by Spark Capital to accelerate development of its Claude assistant, expand product offerings, and fund AI safety research.
OpenAI Governance of Superintelligence Proposal
OpenAI proposes a three-pillar framework for managing superintelligence through international coordination, a global regulatory body similar to the IAEA, and advanced safety research.
Q8-Chat: Efficient Generative AI on Intel Xeon CPUs
Hugging Face and Intel demonstrate that using SmoothQuant to compress LLMs to 8-bit integers allows high-quality chat experiences to run efficiently on single-socket Intel Xeon CPUs.
Large-scale Near-deduplication Behind BigCode
Hugging Face details the use of MinHash and Locality Sensitive Hashing (LSH) to perform large-scale near-deduplication for the BigCode project, demonstrating that removing near-duplicate data improves model performance and training efficiency.
Anthropic and Zoom Partnership and Investment
Anthropic and Zoom have partnered to integrate Claude AI into Zoom's enterprise collaboration products and Zoom Ventures has invested in Anthropic.
RWKV Architecture Integration into Hugging Face Transformers
Hugging Face announced the integration of the RWKV RNN‑Transformer hybrid model into the Transformers library, enabling open‑source, long‑context language models that combine RNN efficiency with transformer performance.
Running Vicuna 13B on AMD GPUs with ROCm
Hugging Face provides a technical guide on deploying the Vicuna 13B open-source chatbot on a single AMD GPU using ROCm and GPTQ 4-bit quantization to reduce memory requirements from 28GB to 7.52GB.
Hugging Face Selected for CNIL Enhanced Support Program
Hugging Face has been selected by the French Data Protection Authority (CNIL) for its Enhanced Support program to improve the implementation of data protection and GDPR compliance in AI development.
Hugging Face Assisted Generation for Low-Latency Text Generation
Hugging Face introduced Assisted Generation, a decoding method that uses a smaller assistant model to predict candidate tokens which are then validated by a larger model, reducing latency by up to 10x in some hardware configurations.
Anthropic Announces 100K Token Context Windows for Claude
Anthropic expanded Claude's context window from 9K to 100K tokens, enabling the model to ingest and analyze hundreds of pages of text in under a minute.
OpenAI releases automated neuron explanation dataset for GPT‑2 using GPT‑4
OpenAI announced a method that uses GPT‑4 to generate and score natural‑language explanations for every neuron in GPT‑2, releasing the resulting dataset, viewer, and code to the research community.
Creating a Coding Assistant with StarCoder – Summary
This article describes how the Hugging Face team turned the 16 B‑parameter StarCoder code‑generation model into a conversational coding assistant (StarChat‑α). By adding special chat tokens, preparing a filtered Open‑Assistant dialogue dataset, and fine‑tuning with DeepSpeed ZeRO‑3, they trained a model that can understand user prompts and generate runnable code (e.g., plots, maps, visualisations). The post also covers token‑izer configuration, loss‑masking for user turns, training setup, evaluation (both benchmark and LLM‑based), limitations, and future directions. The resulting model is publicly available on the Hub.
Claude's Constitution: Implementing Constitutional AI for Model Alignment
Anthropic introduces Constitutional AI, a method for training Claude to be helpful, honest, and harmless using an explicit set of written principles rather than relying solely on implicit human feedback.
A Dive into Text-to-Video Models
Hugging Face provides a technical overview of the evolution of text-to-video generative models, detailing the transition from GANs to Transformers and Diffusion architectures while highlighting the unique challenges of temporal consistency and data scarcity.
StarCoder Release Notes
Hugging Face and ServiceNow's BigCode collaboration released StarCoder and StarCoderBase, 15B parameter Code LLMs trained on permissively licensed data that outperform several open and closed models on programming benchmarks.
Anthropic Research: Distributed Representations, Composition, and Superposition
Anthropic clarifies the distinction between composition and superposition in distributed representations, explaining how these two mechanisms impact generalization and linear computability in neural networks.
Hugging Face Unity API Installation and Usage Guide
The Hugging Face Unity API allows developers to integrate Hugging Face Inference API models into Unity projects via a dedicated package and API wizard.
Training Language Models with Hugging Face Transformers, TensorFlow, and TPUs
Hugging Face provides a scalable end-to-end guide for training masked language models from scratch using TensorFlow and TPU pods, leveraging XLA compatibility for high-performance compute.