Today we are excited to announce SafeCoder – a code assistant solution built for the enterprise. The goal of SafeCoder is to unlock software development productivity for the enterprise, with a fully compliant and self-hosted pair programmer. In marketing    

Large language models have demonstrated remarkable capabilities in understanding and generating human-like text, revolutionizing applications across various domains. However, the demands they place on consumer hardware for training and deployment have become increasingly challenging to meet. 🤗 Hugging Face’s core mission is to democratize good machine learning, and this includes making large models as accessible as possible for everyone. In the same spirit as our bitsandbytes collaboration, we have just integrated the AutoGPTQ library in Transformers, making it possible for […]

Because we are committed to improving the security of our services, we are making changes to the way you authenticate when interacting with the Hugging Face Hub through Git. Starting from October 1st, 2023, we will no longer accept passwords as a way to authenticate your command-line Git operations. Instead, we recommend using more secure authentication methods, such as replacing the password with a personal access token or using an SSH key. Background In recent months, we have    

Code Llama is a family of state-of-the-art, open-access versions of Llama 2 specialized on code tasks, and we’re excited to release integration in the Hugging Face ecosystem! Code Llama has been released with the same permissive community license as Llama 2 and is available for commercial use. Today, we’re excited to release: Models on the Hub with their model cards and license Transformers integration Integration with Text Generation Inference for fast and efficient production-ready inference Integration with Inference Endpoints Integration […]

AudioLDM 2 was proposed in AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining by Haohe Liu et al. AudioLDM 2 takes a text prompt as input and predicts the corresponding audio. It can generate realistic sound effects, human speech and music. While the generated audios are of high quality, running inference    

This article is a cross-post from an originally published post on September 2023 on AWS’s website. Overview Consumer engagement and rewards company Fetch offers an application that lets users earn rewards on their purchases by scanning their receipts. The    

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 […]