#Hugging Face

LoRA employs low-rank matrix adaptations, reducing trainable parameters and optimizing task adaptation in large language models. This approach minimizes storage needs and avoids inference delays. The Python package integrates with PyTorch and the Hugging Face PEFT library, ensuring competitive performance alongside full fine-tuning in benchmarks like GLUE. LoRA adapts specific Transformer elements, like query and value projections, offering flexibility across models such as RoBERTa, DeBERTa, and GPT-2. The 'loralib' can be installed to apply these techniques efficiently.

The llama-recipes repository provides scripts and tutorials for getting started with Meta's Llama models, including the Llama 3.2 Vision and Text models. It includes local, cloud, and on-premises implementation guides. The repository supports multimodal inference and model fine-tuning, accommodating developers interested in integrating advanced language models without unnecessary complexity.

Explore a language model with 1.9 billion parameters and 32K context support, designed for efficient long-document processing. It excels in multilingual translation, particularly in East Asian languages, and offers role-playing capabilities. Recent updates include adaptations for llamacpp and Ollama, with open-source checkpoints available. Evaluation results demonstrate its competitiveness with larger models, making it ideal for chat, translation, and fine-tuning applications.

Explore how LLaVA-pp leverages LLaMA-3 and Phi-3 models to advance visual processing capabilities. Understand the fine-tuning process of these models and how they are showcased on platforms like Hugging Face Spaces and Google Colab. This detailed presentation highlights pretrained and LoRA fine-tuned models, offering solutions for both academic and practical usage. Gain knowledge on setup, integration, and training instructions for the Phi-3-V and LLaMA-3-V models to enhance performance. Discover the project's unique advantages and latest developments in the realm of visual AI technology.

The project demonstrates the implementation of Reinforcement Learning with Human Feedback (RLHF) on the PaLM infrastructure, enabling researchers to explore open-source systems similar to ChatGPT. It provides guidelines on using the PaLM framework, training reward models with human input, and integrating RLHF for improved performance. The contributions of CarperAI and support from Hugging Face are acknowledged, as well as potential enhancements like Direct Preference Optimization.

Utilizing state-of-the-art models, this project converts text into detailed images and integrates smoothly with platforms like Hugging Face and Colab. It enhances image creation with super-resolution, fine-tuning, and auto-selection via CLIP, and supports video generation, offering a robust toolkit for various creative needs.

Bonito, an open-source tool, simplifies the creation of task-specific training datasets from unannotated text to support instruction tuning. It leverages Hugging Face transformers and vllm libraries for seamless synthetic dataset generation. Bonito v1 includes features such as zero-shot task adaptation and supports a diverse range of tasks such as question answering, sentiment analysis, and summarization. Comprehensive documentation and tutorials make it a valuable resource for researchers focusing on model training efficiency.

Discover a vast collection of resources for Ukrainian speech recognition and synthesis, featuring a range of models, datasets, and tools. This community-driven repository offers Speech-to-Text implementations such as wav2vec2 and Citrinet, along with performance benchmarks. Access a variety of datasets compiled from open sources and the community, providing essential materials for research and development in speech technology.

InferLLM offers a streamlined framework for LLM model inference, inspired by llama.cpp, providing kernel optimization and specialized KVstorage for efficient model handling. Compatible with various architectures including Arm, x86, and CUDA, it supports Chinese and English int4 models for versatile deployment on desktops and mobile devices. Recent updates include support for the LLama-2-7B model and enhanced performance on Arm architecture. Integration capabilities include popular models such as ChatGLM, Alpaca, and Baichuan.

PowerPaint offers versatile image inpainting capabilities, supporting tasks like text-guided object inpainting, object removal, and more. Utilizing tailored task prompts, it maintains accuracy and versatility. Recently, PowerPaint v2-1 and v2 models have been open-sourced, with improvements for enhanced performance. Compatible with ControlNet and BrushNet for precise image editing, PowerPaint suits various inpainting needs.

SpanMarker provides a robust framework for Named Entity Recognition, using encoders such as BERT, RoBERTa, and ELECTRA. It integrates with the Hugging Face Transformers library, offering features like model management, hyperparameter tuning, and mixed precision training. SpanMarker enhances usability by supporting different annotation schemes and enables seamless access to the Hugging Face Hub, including a free API for fast deployment. It is suitable for developers aiming to train or utilize high-performance NER models on datasets like FewNERD and OntoNotes5.

The project presents a library that regulates text generation models through JSON schema enforcement, ensuring accurate prompt formulation for function calls. Distinct from OpenAI's approach, it emphasizes schema compliance via the user-friendly Generator class, providing precise and controlled text outputs. It supports easy installation and use, offering the flexibility of custom constraints and prompt extensions. This approach aids developers looking to manage data extraction and formatting within Hugging Face models, enhancing functionality in automated settings.

Open-Sora aims to enhance video production by providing access to sophisticated video generation techniques. True to open-source values, it offers an easy-to-use platform, encouraging innovation and inclusivity in video production. The latest updates, including 3D-VAE and rectified flow in version 1.2, improve video quality and enable functionalities like text-to-video and diverse aspect ratios. Discover its potential through the Gradio demo and utilize its comprehensive processing pipeline for efficient video content creation.

DBRX is a robust open-source language model by Databricks, featuring a Mixture-of-Experts architecture with 132 billion parameters suited for AI development. Offering resources like inference examples and model code, it integrates with platforms such as You.com and Perplexity Labs. It's optimized with libraries like Composer and MegaBlocks and supports both full and LoRA finetuning. Discover its broad capabilities within AI ecosystems through the Hugging Face repository for easy integration and customization.

Explore a straightforward interface for tuning language models with the LoRA method on NVIDIA GPUs. The Simple LLM Finetuner uses the PEFT library to provide easy-to-use tools for dataset handling, parameter tweaking, and evaluating model inference. Suitable for beginners, it supports small datasets and can run on standard Colab instances. Adjust settings with ease to boost model performance with minimal effort.

This neutral guide showcases a broad array of materials for understanding and implementing transformer models, applicable from NLP to computer vision. It features overviews, concise technical insights, tutorials, and applicable examples, suitable for learners and professionals interested in transformers. Highlighted elements include detailed illustrations, technical summaries, and important references such as the 'Attention Is All You Need' paper. The guide also offers practical insights into implementation via resources like the HuggingFace Transformers library.

MergeKit offers an effective solution for merging pre-trained language models with support for algorithms like Linear, SLERP, and Task Arithmetic. It is suitable for resource-constrained settings, functioning on both CPU and GPU with low VRAM requirements. Features include lazy tensor loading and layer-based model assembly. Compatible with models like Llama, Mistral, and GPT-NeoX, it also provides an intuitive GUI on Arcee's platform and supports sharing on the Hugging Face Hub. A versatile YAML configuration enables custom merge strategies.

Explore Magicoder's unique method of using open-source code via OSS-Instruct to improve Large Language Models, generating diverse and low-bias instruction data. Models excel in HumanEval benchmarks and are accessible via Gradio demos, showcasing practical applications. Evaluate robust APIs supporting various coding tasks and performance metrics available on the EvalPlus Leaderboard.

This package integrates Hugging Face transformers like BERT, GPT-2, and XLNet into spaCy, providing a seamless blend into NLP workflows. Designed for spaCy v3, it features multi-task learning, automated token alignment, and customization options for transformer outputs. Installation is user-friendly via pip, compatible with both CPU and GPU. Though direct task-specific heads are unsupported, prediction outputs for text classification are accessible through wrappers.

Discover a toolkit tailored for the optimization and deployment of Mixtral models, offering insights into MoE architecture, performance metrics, training support, and evaluation protocols. It facilitates model fine-tuning and inference via vLLM, accommodating a wide range of AI applications. Access resources like architecture analyses, deployment strategies, and integration guides with frameworks such as Hugging Face. Keep abreast of project updates and engage with the community to enhance AI model performance.

This project integrates WebAssembly with llama.cpp, facilitating AI model inference in browsers without backend or GPU reliance. It offers TypeScript support, high-level APIs for completions and embeddings, and low-level APIs for fine-tuned operations. Recent updates include offline model caching and custom logger support. Models can be loaded in parallel, with inference processes running in a worker to avoid blocking UI interfaces. Demo applications are available on Hugging Face Spaces to showcase its features.

This collection provides a vast selection of resources for Japanese NLP, meticulously curated for developers and researchers. It features detailed lists of over 600 GitHub repositories and 1300 Hugging Face repositories, including Python libraries, language models, and comprehensive corpora. Additionally, the list offers tools for dataset conversion, optical character recognition, and morphological analysis, vital for professionals working with Japanese text. Regular updates and new dataset additions ensure it remains a dynamic and essential resource for advancing Japanese NLP technologies.

Discover the capabilities of foundation and multimodal models in enhancing machine learning outcomes. This project includes models such as YOLO-World, Depth Anything, and CogVLM, which show the versatility and effectiveness of pre-trained frameworks in tasks like zero-shot object detection, depth estimation, and image captioning. Multimodal models handle different data types seamlessly, providing solutions in visual and textual domains. Understand how these AI advancements use large datasets to solve challenges across various fields.

Whisper Playground equips developers to create real-time speech-to-text applications across 99 languages with faster-whisper, Diart, and Pyannote libraries. It integrates easily with backend and frontend setups and provides customizable parameters like model size and language. Supporting Pyannote audio models from Hugging Face Hub, the platform offers advanced segmentation and speaker diarization. Regular updates address known issues, offering an MIT License for development. Discover its capabilities with an online demo.

This comprehensive guide provides instructions for using Llama C++ to develop real-time visual applications, including webcam streaming and image processing. It is optimized for Apple silicon and includes details on installation, model preparation, and running demos, providing a thorough introduction to AI integration in C++ projects.

IMS Toucan is a leading toolkit for multilingual Text-to-Speech Synthesis, supporting over 7000 languages. Created at the Institute for Natural Language Processing, University of Stuttgart, it provides a quick and adjustable solution, functioning efficiently with minimal computing power. Free access through Hugging Face allows exploration of demos and use of a comprehensive multilingual TTS dataset. Easy-to-follow installation instructions are available for Linux, Windows, and Mac, ensuring versatility in training and inference, with the option of using pretrained models for enhanced efficiency.

Text Generation Inference facilitates the efficient deployment of Large Language Models like Llama and GPT-NeoX. It enhances performance with features such as Tensor Parallelism and token streaming, supporting hardware from Nvidia to Google TPU. Key optimizations include Flash Attention and quantization. It also supports customization options and distributed tracing for robust production use.

This guide provides insights into advanced techniques for efficiently fine-tuning large language models with tools like LoRA and Hugging Face. Featuring comprehensive tutorials on various methods such as PEFT, RLHF training, and transformer-based approaches, it offers clear, step-by-step guides for model enhancement—suitable for data scientists and AI researchers seeking to optimize machine learning processes and accuracy.

Parameter-Efficient Fine-Tuning (PEFT) offers a cost-effective way to adapt large pretrained models with reduced computational and storage needs, maintaining high performance similar to fully fine-tuned models. PEFT works with tools like Transformers, Diffusers, and Accelerate, making it versatile for model training and inference across different domains. This method helps in managing large models on consumer hardware by minimizing memory consumption without compromising accuracy.

Discover the Deep Reinforcement Learning resources offered by Hugging Face, featuring detailed mdx files and notebooks aimed at enhancing understanding of AI methodologies. The course includes a structured syllabus and in-depth lectures suitable for those interested in advancing their study of AI. Access comprehensive resources provided by field experts to deepen your learning of reinforcement learning techniques.

This free course offers a comprehensive exploration of Deep Reinforcement Learning, focusing on both theory and practice. It guides the use of popular libraries like Stable Baselines3 and CleanRL, and delves into training agents in various environments such as Minecraft and Doom. The course also facilitates effortless publication of trained agents and provides opportunities to compete in AI challenges, making it an ideal avenue for advancing in AI development.

The LlamaGen project provides innovative image generation capabilities using autoregressive models for both text and class-conditional scenarios, demonstrating significant advancements over traditional diffusion techniques. The project delivers image tokenizers, models scaling from 100M to 3B parameters, and utilizes vLLM for substantial speed improvements during serving. Models are available via online demos and a serving framework maturing image creation efficiency by 300% to 400%, with continuous updates and resources supporting modern AI image generation exploration.

Explore a community-driven, lightweight library designed for efficient data loading and preprocessing in machine learning applications. It offers one-line data loaders with robust preprocessing capabilities for formats such as CSV, JSON, and images. Experience smart caching, memory-mapping, and seamless integration with frameworks like NumPy, Pandas, PyTorch, and TensorFlow. Benefit from built-in support for audio and image data, along with streaming for efficient large dataset access. An ideal tool for researchers needing a fast, flexible solution with efficient disk usage.

The CogVideoX series presents advanced, open-source models for video generation, enabling tasks such as text-to-video, video continuation, and image-to-video. The latest models, CogVideoX-5B and CogVideoX-5B-I2V, enhance video quality and visual effects, providing a flexible framework for GPU fine-tuning. Recent enhancements feature open-source access to key models, boosting inference efficiency and incorporating new prompt optimization tools. Supported by detailed technical documents and community interaction, the series offers innovative video generation capabilities, assisting both developers and researchers.

This repository provides a comprehensive overview of resources, methods, and tools focused on Legal Text Analytics, including tasks like Optical Character Recognition and Legal Norm Classification. It features libraries such as Spacy and NLTK, as well as datasets for various legal applications. Suitable for professionals and researchers aiming to advance NLP capabilities in the legal field, it also covers the latest in Legal Tech using Large Language Models and GPT. Opportunities for community collaboration and contributions are available.

A comprehensive course on Large Language Models (LLMs) leveraging OpenAI and Hugging Face libraries for practical applications such as chatbot design, code generation, and model optimization techniques like PEFT and LoRA. The course is divided into segments covering foundational techniques, project implementation, and enterprise integration without overstating capabilities. Features insights from Medium articles and interactive Colab/Kaggle notebooks aimed at both tech enthusiasts and professionals.

LLaMA-Pro employs progressive block expansion to enhance AI model performance across multiple benchmarks. This open-source project provides training codes and demos, proving effective in code and math tasks. MetaMath-Mistral-Pro surpasses previous models in GSM8k and MATH tests, marking its significance in the field. Its acceptance at ACL 2024 underlines its contribution to AI research and development.

Discover how to create a Discord AI Chatbot using Microsoft's DialoGPT model, trained with The World Ends With You script. The chatbot emulates the unique character Joshua, providing engaging interactions on Discord. The project offers freeCodeCamp tutorials, video guides, a JavaScript version via Discord.js, and a demo on Hugging Face's Model Hub, complete with setup scripts for both Python and JavaScript.

This study presents a comprehensive analysis of the largest in-the-wild jailbreak prompt collection from December 2022 to December 2023. Utilizing the JailbreakHub framework, it compiles data from platforms such as Reddit and Discord, focusing on the risks and behaviors linked to harmful language prompts. The dataset, containing over 15,000 prompts with 1,405 identified as jailbreak prompts, provides crucial insights into the vulnerabilities and possible safeguards of large language models.

VisualGLM-6B is a multi-modal dialog language model supporting images, Chinese, and English, based on ChatGLM-6B with 7.8 billion parameters including visual capabilities from BLIP2-Qformer. The model achieves visual-linguistic interoperability and can be deployed on consumer GPUs by using quantized accuracy. It is pre-trained on 330 million captioned images, optimizing alignment across languages while adhering to open-source protocols. Limitations include image specificity and potential model hallucinations, with plans for future improvements.

The project investigates artificial general intelligence by conducting advanced research and features a collaborative system where language models act as controllers for expert models. Recent releases include Easytool and TaskBench, which aid in interaction simplification and task automation benchmarking for large language models (LLMs). This effort, supporting platforms such as OpenAI's GPT-4 on Azure and integrating with Langchain, eases deployment with tools like Gradio, CLI, and web interfaces. It emphasizes task planning, model selection, execution, and response generation, contributing to AI problem-solving advancements.

Utilize the high-performance Rust-based tokenizers for efficient text processing in research and production environments. Supporting functionalities like normalization with token tracking and pre-processing steps such as truncation, padding, and special token additions, this toolkit is compatible with Python, Node.js, and Ruby, among other languages. Easily customize and train tokenizers with minimal coding efforts. Explore the comprehensive documentation and quick start guides for in-depth understanding.

Text Embeddings Inference provides a high-performance framework for deploying text embeddings and sequence classification models. It includes optimized extraction for popular models like FlagEmbedding and GTE with efficient Docker support, eliminating model graph compilation and facilitating fast booting. This toolkit supports various models including Bert and CamemBERT, offering features like dynamic batching and distributed tracing, suitable for diverse deployment environments.

Unsloth facilitates faster and memory-efficient finetuning for models such as Llama 3.2 and Mistral Small 22B, operating up to five times quicker. The tool offers support for various model versions with accessible Colab and Kaggle notebooks. Models can be finetuned, exported, or uploaded to platforms like Hugging Face without needing specialized hardware. Unsloth's open-source nature enhances finetuning efficiency and serves as a valuable resource, with comprehensive documentation and installation guides available for optimizing usage.

LongLoRA utilizes efficient fine-tuning to enhance long-context language models with techniques like shifted short attention and Flash-Attention compatibility. Supporting models from 7B to 70B and context lengths up to 100k, it integrates an open-sourced dataset, LongAlpaca-12k, while facilitating reduced memory usage through QLoRA. This approach expands models' capability for complex tasks and optimizes computational resources.

Discover resources for optimizing language models using FireAct. The repository provides prompts, demo codes, and datasets tailored for language agent fine-tuning. It supports task exploration, OpenAI API integration, and SERP API utilization. FireAct guides through data generation, Alpaca and GPT format fine-tuning, and supervised learning for enhanced outcomes. Explore a model zoo with Llama family-based multitask models for effective language agent applications.

The ComfyUI adaptation of PhotoMaker provides features such as Lora model integration, flexible image input options, and improved processing speed for efficient photo creation. It includes both online and local model loading options, comprehensive style mixing with a choice of ten styles, and adjustable parameters for better output customization. Based on SDXL models, the project emphasizes enhanced speed, refined workflows, and introduces new style features ideal for producing cinematic, digital art, or Disney-inspired photos.

Learn about the Hughes Hallucination Evaluation Model (HHEM-2.1) and its role in measuring hallucination rates in large language models. This regularly updated leaderboard offers insights into factual consistency and summarization accuracy, presenting detailed rankings and methodologies for performance evaluation. A resourceful tool for those interested in advances in reducing hallucinations in LLMs and improving factual summaries. Comprehensive datasets and prior research are also available for further exploration of LLM evaluation.

Examine how human preferences are incorporated into language model pretraining via Hugging Face Transformers. This method uses annotations and feedback to align models with human values, enhancing their ability to reduce toxicity and meet compliance standards. Learn about methods, configurations, and available pretrained models for tasks including toxicity management, PII detection, and PEP8 adherence, documented using wandb. Leverage this codebase to refine models for better processing of language aligned with human expectations.

Explore a customized arXiv paper digest system leveraging large language models such as GPT. This project efficiently sifts through numerous new papers by assessing their relevance according to user-defined criteria. Users can personalize their experience through a configuration file, specifying research areas and interests, with the option to receive daily HTML digests or email alerts via SendGrid. Engage with the platform via a Hugging Face demo, ensuring privacy with untracked API keys.