This page details the command-line utility for the Embedding Atlas, a tool for exploring large text datasets with metadata. It covers installation, data loading (local and Hugging Face), visualization of embeddings using SentenceTransformers and UMAP, and usage instructions with available options.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
Hugging Face researchers developed an open-source AI research agent called 'Open Deep Research' in 24 hours, aiming to match OpenAI's Deep Research. The project demonstrates the potential of agent frameworks to enhance AI model capabilities, achieving 55.15% accuracy on the GAIA benchmark. The initiative highlights the rapid development and collaborative nature of open-source AI projects.
Hugging Face's initiative to replicate DeepSeek-R1, focusing on developing datasets and sharing training pipelines for reasoning models.
The article introduces Hugging Face's Open-R1 project, a community-driven initiative to reconstruct and expand upon DeepSeek-R1, a cutting-edge reasoning language model. DeepSeek-R1, which emerged as a significant breakthrough, utilizes pure reinforcement learning to enhance a base model's reasoning capabilities without human supervision. However, DeepSeek did not release the datasets, training code, or detailed hyperparameters used to create the model, leaving key aspects of its development opaque.
The Open-R1 project aims to address these gaps by systematically replicating and improving upon DeepSeek-R1's methodology. The initiative involves three main steps:
1. **Replicating the Reasoning Dataset**: Creating a reasoning dataset by distilling knowledge from DeepSeek-R1.
2. **Reconstructing the Reinforcement Learning Pipeline**: Developing a pure RL pipeline, including large-scale datasets for math, reasoning, and coding.
3. **Demonstrating Multi-Stage Training**: Showing how to transition from a base model to supervised fine-tuning (SFT) and then to RL, providing a comprehensive training framework.
The article introduces Hugging Face's Open-R1 project, a community-driven initiative to reconstruct and expand upon DeepSeek-R1, a cutting-edge reasoning language model. DeepSeek-R1, which emerged as a significant breakthrough, utilizes pure reinforcement learning to enhance a base model's reasoning capabilities without human supervision. However, DeepSeek did not release the datasets, training code, or detailed hyperparameters used to create the model, leaving key aspects of its development opaque.
The Open-R1 project aims to address these gaps by systematically replicating and improving upon DeepSeek-R1's methodology. The initiative involves three main steps:
1. **Replicating the Reasoning Dataset**: Creating a reasoning dataset by distilling knowledge from DeepSeek-R1.
2. **Reconstructing the Reinforcement Learning Pipeline**: Developing a pure RL pipeline, including large-scale datasets for math, reasoning, and coding.
3. **Demonstrating Multi-Stage Training**: Showing how to transition from a base model to supervised fine-tuning (SFT) and then to RL, providing a comprehensive training framework.
A detailed guide on creating a text classification model with Hugging Face's transformer models, including setup, training, and evaluation steps.
HunyuanVideo is an open-source video generation model that showcases performance comparable to or superior to leading closed-source models. It includes features like a unified image and video generative architecture, a large language model text encoder, and a causal 3D VAE for spatial-temporal compression.
Hugging Face launches Gradio 5, a major update to its popular open-source tool for creating machine learning applications, aimed at making AI development more accessible and secure for enterprises.
This tutorial covers fine-tuning BERT for sentiment analysis using Hugging Face Transformers. Learn to prepare data, set up environment, train and evaluate the model, and make predictions.
