Today, we’re excited to welcome TII’s Falcon 180B to HuggingFace! Falcon 180B sets a new state-of-the-art for open models. It is the largest openly available language model, with 180 billion parameters, and was trained on a massive 3.5 trillion tokens using TII’s RefinedWeb dataset. This represents the longest single-epoch pretraining for an open model. You can find the model on the Hugging Face Hub (base and chat model) and interact with the model on the Falcon Chat Demo Space. In […]

T2I-Adapter is an efficient plug-and-play model that provides extra guidance to pre-trained text-to-image models while freezing the original large text-to-image models. T2I-Adapter aligns internal knowledge in T2I models with external control signals. We can train various adapters according to different conditions and achieve rich control and editing effects. As a contemporaneous work, ControlNet has a similar function and is widely used. However, it can be computationally expensive to run. This is because, during each denoising step of the reverse diffusion process, both […]

For decades, software developers have designed methodologies, processes, and tools that help them improve code quality and increase productivity. For instance, agile, test-driven development, code reviews, and CI/CD are now staples in the software industry. In “How Google Tests Software” (Addison-Wesley, 2012), Google reports that fixing a bug during system tests – the final testing stage – is 1000x more expensive than    

We aim to give a clear overview of the pros and cons of each quantization scheme supported in transformers to help you decide which one you should go for. Currently, quantizing models are used for two main purposes: Running inference of a large model on a smaller device Fine-tune adapters on top of quantized models So far, two integration efforts have been made and are natively supported in transformers : bitsandbytes and auto-gptq. Note that some additional quantization schemes are […]

In this blog post, we will look at how to fine-tune Llama 2 70B using PyTorch FSDP and related best practices. We will be leveraging Hugging Face Transformers, Accelerate and TRL. We will also learn how to use Accelerate with SLURM. Fully Sharded Data Parallelism (FSDP) is a paradigm in which the optimizer states, gradients and parameters are sharded across devices. During the forward pass, each FSDP unit performs an all-gather operation to get the complete weights, computation is performed […]

What is Würstchen? Würstchen is a diffusion model, whose text-conditional component works in a highly compressed latent space of images. Why is this important? Compressing data can reduce computational costs for both training and inference by orders of magnitude. Training on 1024×1024 images is way more expensive than training on 32×32. Usually, other works make use of a relatively small compression, in the range of 4x – 8x    

Note: This blog post is also available as a documentation page on Transformers. Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains    

Welcome to our latest dive into the world of leaderboards and models evaluation. In a previous post, we navigated the waters of evaluating Large Language Models. Today, we set sail to a different, yet equally challenging domain – Object Detection.    

3D Gaussian Splatting is a rasterization technique described in 3D Gaussian Splatting for Real-Time Radiance Field Rendering that allows real-time rendering of photorealistic scenes learned from small samples of images. This article will break down how it works and what it means for the future of graphics.