At a high level, we aim to build a simpler version of other git hosts' (like GitHub's) PRs and Issues:no forks are involved: contributors push to a special ref branch directly on the source repo no hard distinction between issues and PRs: they are essentially the same so we display ...
LayoutLMv3, the state-of-the-art at the time of writing, achieves an overall mAP score of 0.951 (source).What is Document parsing? A step beyond layout analysis is document parsing. Document parsing is identifying and extracting key information from a document, such as names, items, and ...
AI vs. AI is an open-source tool developed at Hugging Faceto rank the strength of reinforcement learning models in a multi-agent setting. The idea is to get arelative measure of skill rather than an objective oneby making the models play against each other continuously and use the matches ...
Accelerating PyTorch distributed fine-tuning with Intel technologies /blog/assets/36_accelerating_pytorch/04_four_nodes.png user juliensimon Accelerating PyTorch distributed fine-tuning with Intel technologiesFor all their amazing performance, state of the art deep learning models often take a long time...
However, fine-tuning the text encoder requires more memory, so a GPU with at least 24 GB of RAM is ideal. Using techniques like 8-bit Adam, fp16 training or gradient accumulation, it is possible to train on 16 GB GPUs like the ones provided by Google Colab or Kaggle. Fine-tuning ...
Using the Decision Transformer is relatively easy, but as it is an autoregressive model, some care has to be taken in order to prepare the model’s inputs at each time-step. We have prepared both a Python script and a Colab notebook that demonstrates how to use this model.Loading a ...
AutoNLP is a framework created by Hugging Face that helps you to build your own state-of-the-art deep learning models on your own dataset with almost no coding at all. AutoNLP is built on the giant shoulders of Hugging Face's transformers, datasets, inference-api and many other tools....
With the recent rise of generative techniques, machine learning is at an incredibly exciting point in its history. The models powering this rise require even more data to produce impactful results, and thus it’s becoming increasingly important to explore new methods of ethically gathering da...
So for GPT-J it would require at least 48GB of CPU RAM to just load the model.To make the model more accessible, EleutherAI also provides float16 weights, and transformers has new options to reduce the memory footprint when loading large language models. Combining all this it should take...
27_summer_at_huggingface 28_gradio-spaces 29_streamlit-spaces 30_clip_rsicd 31_age_of_ml_as_code 32_1b_sentence_embeddings 33_large_language_models 34_course_launch 35_bert_cpu_scaling_part_2 36_accelerating_pytorch 37_data-measurements-tool 38_getting_started_graphcore...