ChatGPT Pro Release
OpenAI has launched ChatGPT Pro, a $200 monthly subscription plan providing unlimited access to o1, o1-mini, GPT-4o, and a high-compute 'o1 pro mode' for complex problem solving.
OpenAI o1 System Card
OpenAI released the o1 system card detailing its chain-of-thought reasoning model, its safety evaluations, and its Preparedness Framework ratings of medium risk for persuasion and CBRN, low for cybersecurity and model autonomy.
PaliGemma 2 Release Notes
Google has released PaliGemma 2, a new family of vision language models that combine the SigLIP image encoder with the Gemma 2 text decoder across three parameter sizes and multiple input resolutions.
How good are LLMs at fixing their mistakes? A chatbot arena experiment with Keras and TPUs
Hugging Face tested several sub‑10B LLMs on a simple calendar‑API task and found that Gemma 2 9B consistently fixed mistakes with minimal prompting, while smaller and older models struggled or required many corrective turns.
OpenAI and Future Strategic Partnership for Specialist Content
OpenAI and Future have partnered to integrate content from Future's 200-plus specialist media brands into ChatGPT, providing users with reliable, expert information and expanding the publisher's distribution reach.
Morgan Stanley AI Integration and Evaluation Framework
Morgan Stanley collaborated with OpenAI to deploy GPT-4 and Whisper powered tools, achieving 98% advisor adoption through a rigorous evaluation framework focused on reliability and compliance.
AraGen Benchmark and Leaderboard: Introducing 3C3H Evaluation for Arabic LLMs
Hugging Face introduced AraGen, a dynamic benchmark and leaderboard for Arabic LLMs that uses the 3C3H measure to evaluate correctness, completeness, conciseness, helpfulness, honesty, and harmlessness.
Hugging Face CFM Case Study: Fine-tuning Small Models with LLM Insights
Capital Fund Management (CFM) improved financial Named Entity Recognition (NER) accuracy by up to 6.4% and reduced inference costs by up to 80x by using Llama 3.1 to assist in labeling data for fine-tuning compact models like GLiNER and SpanMarker.
Open Source Developers Guide to the EU AI Act
The Hugging Face guide explains how the EU AI Act applies to open source AI developers, outlining obligations for limited‑risk AI systems and non‑systemic‑risk general purpose AI models and pointing to tools for compliance.
QwQ-32B-Preview: Exploring Deep Reasoning Capabilities
Qwen has introduced QwQ-32B-Preview, a model designed for deep reasoning and complex problem-solving through an internal chain-of-thought process.
Hugging Face Hub Storage Rearchitecture
Hugging Face is redesigning its upload and download architecture by introducing a content-addressed store (CAS) to enable byte-level deduplication and improve global transfer speeds for massive AI models.
SmolVLM release notes / what's new
Hugging Face introduces SmolVLM, a 2B parameter Vision Language Model (VLM) that is fully open-source and optimized for low memory footprints and high throughput on edge devices.
You could have designed state of the art positional encoding
The Hugging Face blog post walks through an iterative design of positional encoding for transformers, showing how sinusoidal encoding leads to Rotary Positional Encoding (RoPE) and why it matters for modeling token relationships.
Advancing red teaming with people and AI – OpenAI's approach and application to the o1 family
OpenAI published a white paper detailing its external red teaming process for AI models and applied it to prepare the OpenAI o1 family for public release.
Grab GPT-4o Vision Fine-Tuning for GrabMaps
Grab has implemented GPT-4o vision fine-tuning to automate the localization of traffic signs and lane counting for GrabMaps, improving speed limit sign localization by 13% and lane count accuracy by 20%.
Hugging Face and LLM-jp Launch Open Japanese LLM Leaderboard
Hugging Face and LLM-jp have introduced the Open Japanese LLM Leaderboard, a transparent evaluation platform featuring over 20 datasets to benchmark the performance of Japanese large language models.
Hugging Face Introduces Content-Defined Chunking to Improve Storage Efficiency
Hugging Face announced a content-defined chunking storage approach via its Xet team that reduces storage and transfer costs for large model and dataset files by only uploading modified chunks.
Faster Text Generation with Self-Speculative Decoding
Hugging Face introduces self-speculative decoding via LayerSkip, a method that uses a single LLM's early layers for drafting and later layers for verification to increase generation speed and reduce memory overhead.
FlagEval Debate: A New Multilingual LLM Evaluation Framework
BAAI has launched FlagEval Debate, a dynamic evaluation platform where LLMs compete in multilingual debates to better assess reasoning, logic, and adversarial capabilities.
Rox Revenue Platform Integration with OpenAI
Rox has launched an AI-powered revenue management platform using OpenAI's API to automate data unification, sales workflows, and account monitoring through a system of AI agent swarms.
Hugging Face Judge Arena: Benchmarking LLMs as Evaluators
Hugging Face has launched Judge Arena, a crowdsourced platform that uses human voting to determine which LLMs are most effective as evaluators for grading other AI-generated responses.
OpenAI Opens Paris Office to Expand French AI Ecosystem
OpenAI has opened a new office in Paris to support the rapid adoption of AI across French organizations, startups, and government collaborations.
Qwen2.5-Turbo 1M Token Context Length Release
Qwen has released Qwen2.5-Turbo, which extends the model's context window to 1 million tokens while significantly improving inference speed and maintaining competitive performance on short-sequence tasks.
The Estée Lauder Companies ChatGPT Enterprise Implementation
The Estée Lauder Companies has deployed ChatGPT Enterprise to analyze 75+ years of consumer and clinical data, creating over 240 custom GPTs to accelerate product development and market responsiveness.
Hugging Face Hub Dataset Sharing for Researchers
Hugging Face Hub provides a comprehensive platform for hosting and sharing large-scale ML datasets with integrated tools for exploration, security, and community engagement.
Qwen2.5-Coder Series Release Notes
Qwen has released the Qwen2.5-Coder series, featuring six model sizes from 0.5B to 32B, with the 32B-Instruct model achieving SOTA open-source performance comparable to GPT-4o in coding tasks.
Hugging Face PyCharm Integration
Hugging Face has integrated its Hub directly into PyCharm Professional, allowing developers to discover, insert, and manage machine learning models without leaving their IDE.
Argilla 2.4 release notes / what's new
Argilla 2.4 introduces a no-code UI for importing Hugging Face Hub datasets to build fine-tuning and evaluation datasets through human feedback.
Introducing ChatGPT search
OpenAI has integrated a new web search capability into ChatGPT, allowing users to get fast, timely answers with direct links to relevant web sources.
Promega ChatGPT Adoption Case Study
Promega has integrated ChatGPT across its organization, deploying over 1,400 custom GPTs to accelerate manufacturing, sales, and marketing workflows.
OpenAI SimpleQA benchmark release
OpenAI released SimpleQA, an open‑source benchmark of 4,326 short fact‑seeking questions for evaluating the factual accuracy and calibration of frontier language models.
Decagon Customer Support Automation with OpenAI
Decagon utilizes a multi-model strategy featuring GPT-3.5, GPT-4, and o1-mini to automate up to 91% of global support for enterprise clients.
Universal Assisted Generation: Faster Decoding with Any Assistant Model
Hugging Face and Intel Labs introduced Universal Assisted Generation (UAG), a method that accelerates LLM inference by 1.5x-2.0x by allowing any small model to act as an assistant regardless of its tokenizer.
Digital Green Farmer.chat: Bolstering RAG with LLM-as-a-Judge
Digital Green implemented an LLM-as-a-judge evaluation framework for Farmer.chat, a RAG-based agricultural chatbot, to objectively measure RAG accuracy and optimize model selection across 340k queries.
Aya Expanse Release: Advancing Multilingual LLM Performance
Hugging Face and Cohere For AI have released Aya Expanse, a family of 8B and 32B open-weight models that set new state-of-the-art benchmarks for multilingual performance.
Simplifying, stabilizing, and scaling continuous-time consistency models
OpenAI introduces sCM, a simplified continuous-time consistency model that achieves diffusion-level sample quality in just two sampling steps, providing a ~50x speedup in generation.
HUGS launch: zero‑configuration, hardware‑optimized inference for open LLMs
Hugging Face launched HUGS, a zero‑configuration, hardware‑optimized inference service for open‑source LLMs that runs on NVIDIA, AMD, and soon AWS Inferentia and Google TPUs, enabling enterprises to host models in‑house with an OpenAI‑compatible API.
CinePile 2.0 release: adversarial refinement boosts video QA dataset quality
CinePile 2.0 introduces an adversarial refinement pipeline that upgrades weak QA pairs into vision‑dependent questions, releasing both the improved dataset and the full code, and shows significant performance gains for commercial and open‑source video‑LLMs.
SynthID Text Integration in Transformers v4.46.0
Google DeepMind and Hugging Face have integrated SynthID Text into Transformers v4.46.0, providing a method to apply imperceptible watermarks to AI-generated text for detection via trained classifiers.
OpenAI and Microsoft Partner with Lenfest Institute for AI Collaborative and Fellowship Program
OpenAI and Microsoft have partnered with the Lenfest Institute for Journalism to provide $10 million in funding and credits to help local newsrooms implement AI for business sustainability and innovation.
Deploying Speech-to-Speech on Hugging Face Inference Endpoints
Hugging Face provides a guide for deploying its Speech-to-Speech (S2S) pipeline using custom Docker images on Inference Endpoints to handle high computational demands and reduce latency.
Outlines-core 0.1.0 release notes / what's new
Hugging Face and dottxt have released outlines-core 0.1.0, a Rust port of the Outlines core algorithms for structured generation that improves index compilation speed and portability.
Stable Diffusion 3.5 Large Integration with Diffusers
Hugging Face has integrated Stable Diffusion 3.5 Large, an 8B parameter model available in standard and timestep-distilled versions, into the Diffusers library.
Hugging Face partners with Protect AI to add Guardian scanner for model security
Hugging Face partnered with Protect AI to embed the Guardian scanner into the Hub, automatically detecting dangerous model serialization exploits and improving security for the entire ML community.
Transformers.js v3 release adds WebGPU acceleration, expanded model support, and server‑side JavaScript compatibility
Transformers.js v3 adds WebGPU acceleration, new quantization formats, support for 120 model architectures, and Node.js/Deno/Bun compatibility, enabling fast, on‑device inference in browsers and JavaScript runtimes.
Llama 3.2 in Keras
Llama 3.2 is fully supported in Keras via keras-hub, allowing users to load Hugging Face checkpoints and run models across JAX, PyTorch, or TensorFlow backends.
Hugging Face Transformers Gradient Accumulation Fix
Hugging Face has updated the Transformers Trainer to ensure gradient accumulation is mathematically equivalent to full batch training by correcting how losses are averaged across batches.
OpenAI Evaluating fairness in ChatGPT study summary
OpenAI’s study finds that name‑based harmful stereotypes appear in less than 0.1% of ChatGPT responses and that overall answer quality is consistent across gender and racial name cues.
OpenAI MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
OpenAI introduces MLE-bench, a benchmark using 75 Kaggle competitions to measure the machine learning engineering capabilities of AI agents, with o1-preview achieving bronze medal levels in 16.9% of tasks.
Gradio 5 Security Review
Hugging Face conducted a comprehensive security audit of Gradio 5 with Trail of Bits, fixing all identified vulnerabilities to ensure machine learning applications are safe by default.