Hugging Face and NVIDIA release Optimum-NVIDIA, a library accelerating LLM inference with a single line of code. Native integration of NVIDIA optimizations (TensorRT-LLM, cuDNN) reduces latency and increases throughput without requiring code changes.
Sam Altman returns as CEO of OpenAI. Mira Murati becomes CTO, Greg Brockman resumes role as President. A new board is formed with Bret Taylor as board chair.
Hugging Face and AWS optimize Llama inference on Inferentia2, reducing latency and increasing throughput. Benchmarks demonstrate significant speed gains in token generation for Llama 2 and Llama 3 models.
Hugging Face details N critical technical implementation points for RLHF training with PPO: model architecture, hyperparameters, memory management, and practical optimizations to reproduce ChatGPT-scale results.
DALL·E 3 is now available to ChatGPT Plus and Enterprise users. OpenAI has implemented a safety mitigation stack and shares updates on provenance research.
Hugging Face releases Gradio-Lite, a serverless version of Gradio running entirely in the browser. Uses WebAssembly and Pyodide to execute Python on the client side. Enables building interactive interfaces without backend infrastructure.
Hugging Face accelerates over 130,000 models using ONNX Runtime. Integration optimizes inference on CPU and GPU, reducing latency and memory consumption. Multi-platform support and compatibility with existing models.
OpenAI releases DALL·E 3 system card, documenting the image generation model's capabilities, limitations, and safety measures. The document details risk evaluations, filtering mechanisms, and robustness testing results.
OpenAI releases the system card for GPT-4V, documenting the model's computer vision capabilities, limitations, and identified safety risks. The document details performance evaluations and risk mitigation measures.
ChatGPT now integrates vision, audio, and speech capabilities. Users can have voice conversations with the model, show it images, and receive spoken responses. Rolling out gradually on iOS, Android, and web.
Hugging Face releases a comprehensive guide for fine-tuning Llama 2 70B using PyTorch FSDP. The method leverages distributed sharding to optimize GPU memory and accelerate training across multiple nodes.
Hugging Face introduces Würstchen, a diffusion model for image generation optimized for speed. The model uses a two-stage architecture reducing latency while maintaining visual quality. Code and weights available open-source.
Hugging Face outlines natively supported quantization schemes in Transformers: GPTQ, AWQ, GGUF, bitsandbytes (8-bit, 4-bit). Each method trades off compression versus precision, with direct integration into the library.
Hugging Face introduces T2I-Adapters, lightweight modules for precise control over SDXL image generation without model fine-tuning. Adapters enable guided generation via conditions (sketches, poses, depth) with minimal computational overhead.
OpenAI launches ChatGPT Enterprise, a version with enterprise-grade security and privacy. Delivers the most powerful ChatGPT capabilities to date.
Meta releases Code Llama, a Llama 2 variant specialized in code generation. The model supports multiple programming languages and delivers performance comparable to existing code generation tools for completion and generation tasks.
Hugging Face introduces AutoGPTQ, a quantization method to reduce LLM size. Integration into the transformers library enables model compression while maintaining performance, easing deployment on resource-constrained hardware.
OpenAI enables fine-tuning of GPT-3.5 Turbo, allowing developers to customize the model with their own data for specific use cases.
Hugging Face introduces IDEFICS, an open-source reproduction of a state-of-the-art visual language model. The model combines vision and language for multimodal tasks, with code and weights publicly available.
Hugging Face releases Swift Transformers, a library to run LLMs directly on Apple devices. Compatible with Core ML and optimized for on-device performance, it enables model deployment without network connectivity.
Hugging Face publishes a guide to fine-tune Llama 2 using DPO (Direct Preference Optimization). The method aligns the model to user preferences without explicit reward modeling, reducing computational costs compared to traditional RLHF approaches.
Hugging Face open-sources knowledge distillation code and weights for SD-Small and SD-Tiny, two compressed diffusion models. These variants reduce model size and latency while preserving image generation quality.
Hugging Face releases Agents.js, a JavaScript library enabling LLMs to use tools. It simplifies integration of AI agents into web and Node.js applications through a straightforward API.
Hugging Face launches Inference Endpoints for deploying LLMs in production. Managed service providing auto-scaling, monitoring, and Hub integration. Eliminates infrastructure management for inference.
Study demonstrates transformers are effective for time series forecasting. Introduces Autoformer, a specialized transformer model that outperforms traditional methods on multiple forecasting benchmarks.
Hugging Face optimizes Stable Diffusion for Core ML, enabling fast inference on iPhone, iPad, and Mac. The implementation leverages Apple hardware accelerators to reduce latency and memory consumption.
OpenAI announces API updates: more steerable models, function calling capabilities, longer context, and lower prices.
DuckDB now natively accesses 50,000+ datasets on Hugging Face Hub. Users can query parquet data directly without downloading via simple SQL API.
Falcon, an open-source language model developed by the Technology Innovation Institute (TII), joins the Hugging Face ecosystem. The model is available on the Hub with full support for inference, fine-tuning, and deployment via Inference API.
Hugging Face releases an optimized LLM inference container for Amazon SageMaker, enabling streamlined deployment of language models in production with improved performance and scalability.
Hugging Face and Intel optimize Stable Diffusion for Intel CPUs using NNCF and Optimum. 8-bit quantization reduces model size by 75% and accelerates inference 2-3x on CPU with no significant visual quality loss.
Hugging Face and Microsoft launch Hugging Face Model Catalog on Azure. This integration enables users to access open-source models directly through Microsoft's cloud platform, streamlining deployment and inference in production.
Hugging Face introduces 4-bit quantization with bitsandbytes and QLoRA to reduce LLM memory requirements. The technique enables fine-tuning 65B parameter models on a single 24GB GPU, making training accessible to more users.
Safetensors passed independent security audit confirming its safety. The format becomes default standard for model storage on Hugging Face, gradually replacing legacy formats like pickle.
OpenAI launches ChatGPT app for iOS with conversation sync, voice input, and access to latest model improvements.
Hugging Face introduces RWKV, an RNN model combining transformer advantages: training parallelization and linear inference complexity. Hybrid architecture eliminates the quadratic attention bottleneck.
Hugging Face introduces Assisted Generation, a technique reducing text generation latency by using a fast draft model to validate tokens with a main model. Significant speed improvement without quality loss.
OpenAI uses GPT-4 to automatically generate explanations for neuron behavior in large language models and score those explanations. A dataset of these explanations and scores for every neuron in GPT-2 is released.
Hugging Face releases a comprehensive guide for training language models with the Transformers library using TensorFlow and Google TPUs. The tutorial covers performance optimization and best practices for large-scale distributed training.
Databricks and Hugging Face announce integration delivering up to 40% faster training and fine-tuning of large language models. The partnership optimizes training workflows on Databricks infrastructure.