#Huggingface

Firefly is a versatile tool for training large models, offering pre-training, instruction fine-tuning, and DPO functionality for a broad range of popular models, including Llama3 and Vicuna. It employs methodologies such as full parameter tuning, LoRA, and QLoRA for efficient resource usage, catering to users with limited computing power. Its user-friendly approach allows for straightforward model training with optimized configurations to minimize memory and time consumption. Discover open-source model weights and benefit from proven methods, achieving notable improvements in the Open LLM Leaderboard.

KoGPT by KakaoBrain is a Korean generative pre-trained transformer designed for tasks such as classification, search, summarization, and generation of Korean text. It features over 6 billion parameters with 28 layers, requiring at least 32GB of GPU RAM for optimal functioning. Available in various precision formats, including float16, this reduces memory use. Users should be aware of the potential generation of sensitive content due to training on raw data. Learn more about its specifications for integration into AI applications.

This detailed guide provides step-by-step instructions for installing Vicuna 13B and 7B models on Unix-based systems. The guide is updated for Vicuna 1.5, addressing key changes, and includes tips for managing virtual RAM requirements for the 13B model, which needs ~10GB of CPU RAM. It offers both one-line install commands and a detailed manual installation process, along with necessary packages such as git and wget, to ensure smooth setup and usage.

This project offers a detailed guide to Online Iterative RLHF, a cutting-edge method proven more effective than offline methods. The open-source workflow allows reproduction of advanced LLMs using only open-source data, achieving results on par with or better than LLaMA3-8B-instruct. It includes comprehensive setup instructions covering fine-tuning, reward modeling, data generation, and iterative training.

The project provides an in-depth guide to fine-tuning Large Language Models (LLMs) using a famous quotes dataset, with support for advanced methods like DeepSpeed, Lora, and QLora. It includes a comprehensive Docker walkthrough to integrate Nvidia-docker for GPU acceleration on Linux systems with modern Nvidia GPUs. The repository offers both updated and legacy code, catering to users with varying familiarity levels, and professional assistance is available if needed.

Explore instruction-finetuned text embeddings designed for various tasks and domains without the need for additional finetuning. This project outlines improvements to existing repositories and introduces a model excelling in 70 embedding tasks. Learn about straightforward installation, setup, and applications in classification, retrieval, and more, evaluated on benchmarks such as MTEB, Billboard, and Prompt Retrieval.

EasyLM provides an efficient framework for pre-training, fine-tuning, evaluating, and deploying large language models with JAX/Flax. It supports TPU/GPU scaling across multiple hosts using JAX's pjit utility and integrates with Huggingface's tools for straightforward customization. Models like LLaMA and its successors are available. Participate in discussions on JAX-based LLM training on Discord for further insights.

CMLM-ZhongJing, inspired by historical Chinese physician Zhang Zhongjing, integrates traditional Chinese medicine with modern AI technologies to create a dependable tool for medical diagnostics. Emphasizing the need for expert guidance, it leverages Baichuan2-13B-Chat and Qwen1.5-1.8B-Chat for enhanced understanding in TCM. The fine-tuned model is available on Huggingface for download and supports fast GPU inference. Additionally, a Gradio-powered web demo facilitates interactive dialogues, underpinning precise data construction crucial for fields demanding high accuracy such as medicine and law.

Explore a tool that converts text into videos by merging images, audio, and subtitles. Utilizing stable-diffusion for visuals and edge-tts for audio, this solution creates multimedia content via opencv and ffmpeg, supporting MP4 format. With OpenAI and huggingface models for enhanced imagery, the tool is ready for Docker and macOS development environments.

This article introduces a major advancement in computational pathology with the UNI model, a self-supervised model trained on a vast dataset of over 100 million histopathology images across 20 different tissue types. It addresses the complexities of annotating high-resolution whole-slide images and excels in 34 clinical tasks, including resolution-agnostic tissue classification and few-shot classification of up to 108 cancer types. Learn how UNI enhances data efficiency and adapts to clinical workflows, surpassing existing models in performance across diverse diagnostic challenges.

GFPGAN offers a versatile tool for real-world blind face restoration by leveraging pretrained face GAN models, such as StyleGAN2, to deliver natural-looking improvements even on low-resolution images. Latest updates provide models like V1.3 and V1.4 for finer results, and compatibility with platforms such as Huggingface Spaces. The algorithm also supports background enhancement via Real-ESRGAN and runs on various operating systems, ideal for projects requiring comprehensive face and image restoration.

Explore an innovative approach to synthesizing quality alignment data using aligned language models without prompt engineering. This method utilizes pre-query templates for enhanced data generation, creating user queries and model responses for comprehensive alignment datasets that improve model performance. Discover recent updates and datasets like Qwen2.5 and Magpie Llama-3.1, demonstrating state-of-the-art performance, and learn about the methodologies that provide open access to AI alignment processes.

Explore how the SimCSE framework, employing unsupervised and supervised methods, advances sentence embeddings with contrastive learning. The unsupervised model leverages dropout as noise to predict input sentences, while the supervised model employs entailment and contradiction pairs from NLI datasets for better embeddings. Easy installation via PyPI and Huggingface compatibility ensures seamless model integration. Recent updates highlight EMNLP acceptance and enhanced model performance. Investigate SimCSE for optimizing sentence embeddings.

Discover the application of large language models in creating varied robotic simulation tasks through an LLM code generation pipeline. This project supports the establishment of comprehensive simulation environments and objectives, enhancing simulation abilities. Notable aspects include straightforward installation, task management, and policy benchmarking for multitask strategies. The solution provides comprehensive support for training and evaluation with advanced models like GPT-4 and finetuned Code-LLAMA, offering a robust platform for developers in robotic simulation.

Discover Jlama, a modern Java inference engine enabling the integration of Llama, BERT, and GPT-2 models. Features include paged attention, tool calling, distributed inference, and compatibility with the new Vector API for enhanced processing speeds. Easily integrate these capabilities into Java projects using Langchain4j while benefiting from extensive documentation and community support.

YuzuMarker.FontDetection introduces a model for recognizing fonts in Chinese, Japanese, and Korean texts. Utilizing an open-source dataset available on Huggingface, the project includes instructions for data preparation and model training to support effective font classification. With options for online demos and Docker deployment, this tool aids developers and researchers in text detection and font analysis across Asian scripts without exaggeration.

Explore a curated list of open-source Korean LLMs, featuring models like Polyglot-Ko and KoAlpaca, built on architectures such as GPT-NeoX and Llama-2. This resource includes information on model sizes, creators, base models, and commercial usage conditions. It supports your exploration and potential implementation of Korean language processing tools. Access models available on platforms like Huggingface, and participate by sharing updates or new LLMs.

The project focuses on training a compact 0.2B parameter Chinese generative language model suitable for environments with limited computational resources. The model training is feasible with just 4GB GPU and 16GB RAM, supporting extensive methods like data cleaning, tokenizer training, SFT fine-tuning, and RLHF optimization using open-source datasets. The Huggingface frameworks such as transformers and accelerate assist in the process. Further, the project facilitates uninterrupted training continuation and offers support for downstream task fine-tuning, with regular updates enhancing its utility for researchers in scalable language model implementations.

Calflops provides a complete tool for calculating theoretical FLOPs, MACs, and parameters in diverse neural networks such as CNNs, RNNs, and large language models. This tool offers efficient analysis of Pytorch-based models with detailed performance metrics for each submodule, facilitating a deeper understanding of performance costs. The tool's integration with Huggingface enhances usability by enabling computations without full model downloads. Drawing inspiration from libraries like ptflops, deepspeed, and hf accelerate, Calflops improves FLOPs calculations and supports Transformer models, making it a key asset for performance analysis and optimization.

Fastembed-rs provides a Rust-based solution for generating embeddings with ONNX inference, supporting synchronous operations and parallel embeddings using Rayon without Tokio. It integrates @huggingface/tokenizers for fast text encoding and offers high-performing text and image embedding models, like Flag Embedding. The library is lightweight, with no hidden dependencies, and achieves high accuracy, outperforming models like OpenAI Ada-002. It's versatile, with support for custom models and local file inference.

Learn how GPT models, including Bi-Encoders and Cross-Encoders, enhance semantic search. Explore multilingual support and tools like Sentence Transformers for improved performance. Discover the latest AI models GRIT & GritLM for effective search solutions, and access examples for implementing pre-trained models, batch processing, and weighted mean pooling to optimize search accuracy, catering to developers aiming to upgrade their AI search tools.

Convert text to graphics using AI technology. SolidUI offers 2D and 3D graphic models and scenes by merging natural language processing with computer graphics. Its unique Vincent graph language model benefits from reinforcement learning for improved accuracy. The platform supports containerized deployment, various data sources, Huggingface collaboration, and plug-in robotics for enhanced visualization tool development.

This workshop explores large language models with Megatron-GPT2 architecture through detailed trainings and experiments. It addresses model scaling, training dynamics, and instabilities, supported by extensive documentation and logs. Providing resources like code repositories and training scripts, the project fosters transparency and collaboration within the AI community, guiding toward future advancements in language models.

The project employs Docker containers with GPU acceleration for running Stable Diffusion, simplifying the tasks of text-to-image and image-to-image transformations using models from Huggingface. It mandates a CUDA-capable GPU with 8GB+ VRAM and supports functionalities like depth-guided diffusion, inpainting, and upscaling. A Huggingface user token is required for model access, with pipeline management via an intuitive script. Its configurable nature suits both high-performance and less robust systems, enhancing resource-efficient image rendering for developers and artists.

Explore TensorFlow 2's capabilities for state-of-the-art speech synthesis with models like Tacotron-2, FastSpeech, and MelGAN. The project enhances training efficiency and inference speed, making it suitable for real-time use on mobile and embedded systems. It supports multiple languages and offers comprehensive documentation for easy integration. Learn more about innovations such as the HiFi-GAN vocoder and guided attention loss for high-quality speech synthesis.

The DeepSeekMath 7B model offers enhanced mathematical reasoning, performing at 51.7% on the MATH benchmark independently of external tools. This model is proficient in solving mathematics problems with in-depth step-by-step reasoning, supporting complex math solutions, natural language processing, and coding capabilities. Equipped with comprehensive pre-training and using innovative techniques such as Group Relative Policy Optimization, it achieves superior performance compared to other open-source models. Available for both academic and commercial use, it can be accessed on platforms like Hugging Face under the MIT License.

DialogStudio provides a rich and diverse collection of dialog datasets that support conversational AI research and model training. These datasets are available on Huggingface and are continually updated for better accessibility and evaluation through various metrics. The latest version features refined models for enhanced conversational agent development.

OpenRLHF is a high-performance RLHF framework built on Ray, DeepSpeed, and HuggingFace Transformers. It focuses on simplicity and compatibility, enhancing training with vLLM and PPO optimizations for models over 70 billion parameters, supporting advanced distributed systems and multi-GPU setups to boost training stability.

Explore a comprehensive toolkit for contrastive learning that ensures efficient training with Flash Attention and multi-GPU features. Utilize GradCache to handle large batch sizes and delve into Masked Language Modeling pretraining. The toolkit includes Matryoshka Representation Learning for adaptable embedding sizes and supports CLIP and LiT models, along with Vision Transformers. Tailored for researchers with access to 'nomic-embed-text-v1' dataset and pretrained models, it enables effective training and fine-tuning of vision-text models. Engage with the Nomic Community for additional collaboration and insights.

Gazelle is an open-source Joint Speech Language Model built on the foundation of Huggingface's Llava implementation. It invites community participation for further enhancements. The project features multiple checkpoints, such as v0.2 and v0.1, accessible on Huggingface. Governed by Apache 2.0 and Llama 2 licenses, users must comply with respective terms. While Gazelle counters jailbreaks and adversarial threats, its use in production is not advised. Connect with the development team on Discord and explore comprehensive insights in their blog.

Gigax elevates NPC interactions using fine-tuned AI models for seamless speech and actions in gaming environments. Benefit from structured outputs and explore new local server modes and APIs for improved quest and memory management. Discover Gigax models on Huggingface for scalable solutions.

BertViz is a tool for visualizing attention mechanisms in Transformer models such as BERT, GPT-2, and T5. It supports Jupyter and Colab environments via a Python API, compatible with Huggingface models. By enhancing the Tensor2Tensor framework, BertViz provides unique insights through head, model, and neuron views, aiding researchers and developers in exploring attention layers.

EduChat is a sophisticated chatbot system designed for intelligent education, leveraging pre-trained large-scale language models fine-tuned with a variety of educational data. It offers services such as automated assignment grading, emotional support, tutoring, and exam guidance to improve personalized education. Developed by the EduNLP team at East China Normal University, the project focuses on aligning educational values and providing comprehensive educational tools. Its features cater to teachers, students, and parents, promoting fair and engaging education.

GLM-4-9B, developed by Zhipu AI, delivers outstanding performance in comparison to other leading models like GPT-4-turbo and Llama-3-8B, across tasks such as dialogue, mathematics, reasoning, and code execution. As an open-source model, it supports multilingual capabilities and can process up to 1 million tokens in chat environments, providing high-resolution dialogue in two languages. Recent updates include OpenAI API compatibility and improvements in generating extended content. Explore its capabilities demonstrated through various multilingual and multimodal assessments.

StableTTS is a state-of-the-art flow-matching TTS model that integrates DiT, supporting efficient speech generation across Chinese, English, and Japanese. This 31M parameter model enhances audio quality and supports CFG and FireflyGAN vocoders, with improvements in the Chinese text frontend. The newly released version 1.1 introduces features like U-Net-inspired skip connections and a cosine timestep scheduler, all within a single multilingual checkpoint. Designed for user-friendly training, it simplifies data preparation and finetuning, making it an adaptable solution for varied audio generation applications.

This unofficial adaptation of InstantID for ComfyUI provides powerful tools including pose reference for enhanced ID creation. Version 2.0 enables improved model management through automatic downloads from Huggingface Hub and local storage. Users can choose from various styles, apply InsightFace models, and enjoy compatibility with diverse GPUs. Enhanced code efficiency and new functionalities refine the image generation process, ensuring precise styling and consistent performance. A detailed installation guide facilitates quick setup, supported by comprehensive testing for assured reliability.

DeepSeekMoE 16B, utilizing a Mixture-of-Experts architecture, enhances computational efficiency with a reduction to 40% of operations. Matching the performance of models like LLaMA2 7B, its Base and Chat versions support English and Chinese, enabling deployment on a single GPU without quantization. Available under specific licensing for research and commercial applications.