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.
Hugging Face releases a hands-on guide for training LLaMA with RLHF (Reinforcement Learning from Human Feedback). The tutorial covers full implementation from data preparation to model optimization, with reproducible code and concrete examples.
Hugging Face demonstrates fast inference of BLOOMZ on Habana Gaudi2 accelerator. The 176B model achieves 1,000 tokens/sec with hardware-specific optimizations. Reproducible benchmark on Habana infrastructure.
OpenAI details a ChatGPT outage on March 20. The article covers technical root causes, corrective actions taken, and findings from the internal investigation.
OpenAI rolls out initial support for plugins in ChatGPT. These tools, designed specifically for language models with safety as a core principle, enable ChatGPT to access up-to-date information, run computations, or use third-party services.
OpenAI releases analysis on the potential labor market impact of large language models. The study examines how language models could affect different sectors and professions by assessing exposure to AI-automatable tasks.
Hugging Face demonstrates RLHF fine-tuning of a 20B model on 24GB consumer GPU (RTX 4090). Uses quantization and memory optimizations to reduce requirements from 780GB to 24GB. Code and benchmarks available.
Hugging Face integrates ControlNet into Diffusers library, enabling precise spatial control over image generation models. ControlNet adds conditioning inputs (depth maps, poses, edges) to guide image synthesis without retraining.
Hugging Face releases Swift Diffusers, a native Stable Diffusion implementation optimized for macOS. The library leverages Metal Performance Shaders to accelerate inference on Apple Silicon GPUs, enabling fast image generation directly on Mac without external server dependency.
Hugging Face introduces BLIP-2, a zero-shot image-to-text generation model. The model combines a vision encoder with an LLM to describe images in natural language without additional fine-tuning.
Hugging Face introduces PEFT, a library for parameter-efficient fine-tuning. It drastically reduces resources needed to adapt large models via LoRA, QLoRA and other techniques without retraining all weights.
Hugging Face introduces SpeechT5, a unified model for speech synthesis, recognition, and additional audio tasks. The model uses an encoder-decoder architecture and demonstrates competitive performance across multiple speech benchmarks.
Hugging Face introduces AI vs. AI, a multi-agent competition system based on deep reinforcement learning. The platform enables models to compete in adversarial environments to evaluate and improve reinforcement learning capabilities.
OpenAI launches ChatGPT Plus, a pilot subscription plan for its conversational AI. The service enables conversations, follow-up question responses, and challenges to incorrect assumptions.
Hugging Face presents LoRA (Low-Rank Adaptation) for efficient Stable Diffusion fine-tuning. This method drastically reduces trainable parameters and memory requirements, enabling fine-tuning on standard hardware.
Hugging Face announces Optimum integration with ONNX Runtime to accelerate model training. The solution reduces latency and improves throughput through computation graph optimization and automatic quantization.
Hugging Face introduces Mask2Former and OneFormer for universal image segmentation. Mask2Former improves performance on semantic, instance, and panoptic segmentation tasks. OneFormer unifies all three tasks in a single trainable model.
CLIPSeg combines CLIP with segmentation for zero-shot image segmentation. The model uses text descriptions to identify and isolate specific objects or regions in images without requiring prior training or annotations.
OpenAI introduces Point-E, a system generating 3D point clouds from complex text prompts. The model uses a two-stage process: intermediate image generation followed by 3D geometry conversion. Point-E produces results 60x faster than existing 3D diffusion methods.
Habana Gaudi2 outperforms Nvidia A100 80GB in training and inference speed across multiple LLM models. Comparative benchmark showing performance gains on Llama 2, Mistral and other architectures.
Hugging Face releases a comprehensive guide for training Stable Diffusion with DreamBooth using the Diffusers library. The method enables model personalization on specific images through resource-efficient fine-tuning.
OpenAI makes DALL·E API available in public beta. Developers can now build applications using the image generation model.
OpenAI publishes research on scaling laws for reward model overoptimization. Researchers quantify performance degradation when excessively optimizing a reward function, with implications for reinforcement learning training and model alignment.
Stable Diffusion is now available in JAX/Flax. This implementation provides improved performance and scalability for inference and training on TPU and GPU. Code is open-source on Hugging Face.
DALL·E is now available without waitlist for new users. OpenAI has improved safety systems and applied lessons learned from gradual deployment.
SetFit is an efficient few-shot learning method that eliminates the need for prompts. It leverages sentence embeddings and lightweight fine-tuning to rapidly adapt models to new tasks with minimal examples.
OpenAI launches outpainting for DALL·E, enabling users to extend generated images beyond their original boundaries. Feature now available on the platform.
Hugging Face introduces Diffusers, a library for implementing and using diffusion models like Stable Diffusion. The tool simplifies access to image generation models with a modular API and pre-configured pipelines.
Hugging Face releases a guide on 8-bit matrix multiplication for large-scale transformers using transformers, accelerate, and bitsandbytes libraries. Quantization technique reduces memory footprint and accelerates inference with minimal precision loss.
Hugging Face introduces Skops, a library for saving, loading and sharing scikit-learn models with versioning and traceability. Native integration with Hugging Face Hub to enable collaboration and reproducibility.
Hugging Face launches Private Hub, a platform enabling enterprises to build ML models in a private, secure environment. Provides access to open-source models, fine-tuning tools, and dedicated infrastructure without public exposure.
Nyströmformer approximates self-attention in linear time and memory using the Nyström method. This technique reduces the quadratic complexity of standard transformers, enabling processing of longer sequences with lower computational requirements.
OpenAI presents an efficient training method for language models capable of filling in text in the middle of sequences. The technique reduces computational costs while maintaining performance on standard tasks.
OpenAI releases a hazard analysis framework for code synthesis LLMs. The work assesses potential dangers of code-generating models, proposes evaluation methods and mitigation measures to improve safety.
DALL·E enters beta with access rolling out to 1 million waitlist users over coming weeks. Users get monthly refillable free credits and can purchase additional credits in 115-generation increments for $15.
Hugging Face details the infrastructure and techniques used to train BLOOM, a 176B-parameter multilingual language model. Technical article covering distributed optimization, memory management, and large-scale training challenges.
Hugging Face integrates DeepSpeed to accelerate large model training. The solution optimizes memory and speed through model partitioning, gradient optimization, and mixed precision.
Hugging Face launches an evaluation platform integrated into the Hub for benchmarking models directly. Users can create custom evaluations, compare performance, and share results without external infrastructure.
Hugging Face Optimum enables conversion of Transformer models to ONNX format to optimize inference performance. The tool automates conversion and provides quantization and optimization options for efficient deployment on CPU and GPU.
Gradio 3.0 released. The ML model web interface platform introduces new architecture, redesigned components, and improved performance for rapid AI application deployment.