Best AI tools for< Load Model In Huggingface >

Avaturn is a realistic 3D avatar creator that uses generative AI to turn a 2D photo into a recognizable and realistic 3D avatar. With endless options for avatar customization, you can create a unique look for each and everyone. Export your avatar as a 3D model and load it in Blender, Unity, Unreal Engine, Maya, Cinema4D, or any other 3D environment. The avatars come with a standard humanoid body rig, ARKit blendshapes, and visemes. They are compatible with Mixamo animations and VTubing software.

Ermine.ai is an AI-powered tool for local audio recording and transcription. It allows users to transcribe audio files with high accuracy using a transcription model that is loaded and initialized in the user's browser. The tool currently supports Chrome browser and English transcription only. Users can easily transcribe audio files by allowing microphone access and waiting for the model to load. Ermine.ai provides a convenient solution for transcription needs, offering a seamless and efficient transcription process.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

Milo is an AI-powered co-pilot for parents, designed to help them manage the chaos of family life. It uses GPT-4, the latest in large-language models, to sort and organize information, send reminders, and provide updates. Milo is designed to be accurate and solve complex problems, and it learns and gets better based on user feedback. It can be used to manage tasks such as adding items to a grocery list, getting updates on the week's schedule, and sending screenshots of birthday invitations.

VoiceGPT is an Android app that provides a voice-based interface to interact with AI language models like ChatGPT, Bing AI, and Bard. It offers features such as unlimited free messages, voice input and output in 67+ languages, a floating bubble for easy switching between apps, OCR text recognition, code execution, image generation with DALL-E 2, and support for ChatGPT Plus accounts. VoiceGPT is designed to be accessible for users with visual impairments, dyslexia, or other conditions, and it can be set as the default assistant to be activated hands-free with a custom hotword.

Kolank is an AI tool that offers a unified API with features such as load balancing, fallbacks, cost and performance metrics. Users can access models for generating text, images, and videos through simple API calls. The platform supports multiple programming languages like Python, JavaScript, and Curl, making it easy for developers to integrate AI capabilities into their applications.

Fastbreak is an AI Assistant application designed to help users win Requests for Proposals (RFPs) and Requests for Information (RFIs) by automating the response process. It accelerates completion time, optimizes access to relevant information, streamlines leveraging domain expertise, and allows users to relax by saving time and winning more business. The application uses contextual smarts to understand the semantic meaning of questions and synthesize answers based on previous responses, white papers, and product documents. Fastbreak is a valuable tool for sales teams, product marketing professionals, proposal managers, IT security experts, startup founders, and finance professionals.

WebPilot is an AI tool designed to enhance your GPTs by enabling them to perform various tasks such as opening URL/file links, using multiple search engines, accessing all types of websites, loading dynamic web content, and providing enhanced answers. It offers a super easy way to interact with webpages, assisting in tasks like responding to emails, writing in forms, and solving quizzes. WebPilot is free, open-source, and has been featured by Google Extension Store as an established publisher.

Parade is a capacity management platform designed for freight brokerages and 3PLs to streamline operations, automate bookings, and improve margins. The platform leverages advanced AI to optimize pricing, bidding, and carrier management, helping users book more loads efficiently. Parade integrates seamlessly with existing tech stacks, offering precise pricing, optimized bidding, and enhanced shipper connectivity. The platform boasts a range of features and benefits aimed at increasing efficiency, reducing costs, and boosting margins for freight businesses.

The website offers an AI-powered tool called SwapFans that allows users to load balance and receive discounts. Users can easily FaceSwap any social media videos and swap entire Instagram and TikTok accounts with high-speed FaceSwap AI. The tool is designed to help users manage their social media presence effectively and efficiently.

PixieBrix is an AI engagement platform that allows users to build, deploy, and manage internal AI tools to drive team productivity. It unifies AI landscapes with oversight and governance for enterprise scale. The platform is enterprise-ready and fully customizable to meet unique needs, and can be deployed on any site, making it easy to integrate into existing systems. PixieBrix leverages the power of AI and automation to harness the latest technology to streamline workflows and take productivity to new heights.

TLDRai.com is an AI tool designed to help users summarize any text into concise and easy-to-digest content, enabling them to free themselves from information overload. The tool utilizes AI technology to provide efficient text summarization services, making it a valuable resource for individuals seeking quick and accurate summaries of lengthy texts.

The website is currently displaying a '403 Forbidden' error, which indicates that the server is refusing to respond to the request. This error message is typically shown when the user is trying to access a webpage or resource that they are not authorized to view. The 'openresty' mentioned in the text is a web platform based on NGINX and LuaJIT, used for building scalable web applications and services. It is often used for high-performance web applications and APIs.

Merlin AI is a YouTube transcript tool that allows users to create summaries of YouTube videos. It is easy to use and can be added to Chrome as an extension. Merlin AI is powered by an undocumented API and features the latest build.

Daxtra is an AI-powered recruitment technology tool designed to help staffing and recruiting professionals find, parse, match, and engage the best candidates quickly and efficiently. The tool offers a suite of products that seamlessly integrate with existing ATS or CRM systems, automating various recruitment processes such as candidate data loading, CV/resume formatting, information extraction, and job matching. Daxtra's solutions cater to corporates, vendors, job boards, and social media partners, providing a comprehensive set of developer components to enhance recruitment workflows.

Widya Robotics is an AI, Automation, and Robotics solutions provider that offers a range of innovative products and solutions for various industries such as construction, manufacturing, retail, and traffic and transportation. The company specializes in technologies like LiDAR for load scanning, gas monitoring, and AI-driven solutions to enhance efficiency, safety, and profitability for businesses. Widya Robotics has received recognition for its cutting-edge technology and commitment to helping companies achieve their financial and branding goals.

Lex Fridman is an AI tool developed by Lex Fridman, a Research Scientist at MIT, focusing on human-robot interaction and machine learning. The tool offers various resources such as podcasts, research publications, and studies related to AI-assisted driving data collection, autonomous vehicle systems, gaze estimation, and cognitive load estimation. It aims to provide insights into the safe and enjoyable interaction between humans and AI in driving scenarios.

Epicflow is an AI-based multi-project and resource management software designed to help organizations deliver more projects on time with available resources, increase profitability, and make informed project decisions using real-time data and predictive analytics. The software bridges demand and supply by matching talent based on competencies, experience, and availability. It offers features like AI assistant, What-If Analysis, Future Load Graph, Historical Load Graph, Task List, and Competence Management Pipeline. Epicflow is trusted by leading companies in various industries for high performance and flawless project delivery.

Knowbo is a custom chatbot tool that allows users to create a chatbot for their website in just 2 minutes. The chatbot learns directly from the website or documentation, providing up-to-date information to users. With features like easy deployment, chat history tracking, and customization options, Knowbo aims to revolutionize customer experience by reducing the load on support teams and offering a seamless way for users to get their questions answered quickly.

The website promptsplitter.com is experiencing an Argo Tunnel error on the Cloudflare network. Users encountering this error are advised to wait a few minutes and try again. For website owners, it is recommended to ensure that cloudflared is running and can reach the network, and consider enabling load balancing for the tunnel.

SoM-LLaVA is a new data source and learning paradigm for Multimodal LLMs, empowering open-source Multimodal LLMs with Set-of-Mark prompting and improved visual reasoning ability. The repository provides a new dataset that is complementary to existing training sources, enhancing multimodal LLMs with Set-of-Mark prompting and improved general capacity. By adding 30k SoM data to the visual instruction tuning stage of LLaVA, the tool achieves 1% to 6% relative improvements on all benchmarks. Users can train SoM-LLaVA via command line and utilize the implementation to annotate COCO images with SoM. Additionally, the tool can be loaded in Huggingface for further usage.

InternLM is a powerful language model series with features such as 200K context window for long-context tasks, outstanding comprehensive performance in reasoning, math, code, chat experience, instruction following, and creative writing, code interpreter & data analysis capabilities, and stronger tool utilization capabilities. It offers models in sizes of 7B and 20B, suitable for research and complex scenarios. The models are recommended for various applications and exhibit better performance than previous generations. InternLM models may match or surpass other open-source models like ChatGPT. The tool has been evaluated on various datasets and has shown superior performance in multiple tasks. It requires Python >= 3.8, PyTorch >= 1.12.0, and Transformers >= 4.34 for usage. InternLM can be used for tasks like chat, agent applications, fine-tuning, deployment, and long-context inference.

HQQ is a fast and accurate model quantizer that skips the need for calibration data. It's super simple to implement (just a few lines of code for the optimizer). It can crunch through quantizing the Llama2-70B model in only 4 minutes! 🚀

AQLM is the official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization. It includes prequantized AQLM models without PV-Tuning and PV-Tuned models for LLaMA, Mistral, and Mixtral families. The repository provides inference examples, model details, and quantization setups. Users can run prequantized models using Google Colab examples, work with different model families, and install the necessary inference library. The repository also offers detailed instructions for quantization, fine-tuning, and model evaluation. AQLM quantization involves calibrating models for compression, and users can improve model accuracy through finetuning. Additionally, the repository includes information on preparing models for inference and contributing guidelines.

AnglE is a library for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. It also serves as a general sentence embedding inference framework, allowing for inferring a variety of transformer-based sentence embeddings. The library supports various loss functions such as AnglE loss, Contrastive loss, CoSENT loss, and Espresso loss. It provides backbones like BERT-based models, LLM-based models, and Bi-directional LLM-based models for training on single or multi-GPU setups. AnglE has achieved significant performance on various benchmarks and offers official pretrained models for both BERT-based and LLM-based models.

MFLUX is a line-by-line port of the FLUX implementation in the Huggingface Diffusers library to Apple MLX. It aims to run powerful FLUX models from Black Forest Labs locally on Mac machines. The codebase is minimal and explicit, prioritizing readability over generality and performance. Models are implemented from scratch in MLX, with tokenizers from the Huggingface Transformers library. Dependencies include Numpy and Pillow for image post-processing. Installation can be done using `uv tool` or classic virtual environment setup. Command-line arguments allow for image generation with specified models, prompts, and optional parameters. Quantization options for speed and memory reduction are available. LoRA adapters can be loaded for fine-tuning image generation. Controlnet support provides more control over image generation with reference images. Current limitations include generating images one by one, lack of support for negative prompts, and some LoRA adapters not working.

Prometheus-Eval is a repository dedicated to evaluating large language models (LLMs) in generation tasks. It provides state-of-the-art language models like Prometheus 2 (7B & 8x7B) for assessing in pairwise ranking formats and achieving high correlation scores with benchmarks. The repository includes tools for training, evaluating, and using these models, along with scripts for fine-tuning on custom datasets. Prometheus aims to address issues like fairness, controllability, and affordability in evaluations by simulating human judgments and proprietary LM-based assessments.

Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. It outperforms other static embedding models like GLoVe and BPEmb, is lightweight with only `numpy` as a major dependency, offers fast inference, dataset-free distillation, and is integrated into Sentence Transformers, txtai, and Chonkie. Model2Vec creates powerful models by passing a vocabulary through a sentence transformer model, reducing dimensionality using PCA, and weighting embeddings using zipf weighting. Users can distill their own models or use pre-trained models from the HuggingFace hub. Evaluation can be done using the provided evaluation package. Model2Vec is licensed under MIT.

Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in OpenVINO™ with a minimal accuracy drop. It is designed to work with models from PyTorch, TorchFX, TensorFlow, ONNX, and OpenVINO™. NNCF offers samples demonstrating compression algorithms for various use cases and models, with the ability to add different compression algorithms easily. It supports GPU-accelerated layers, distributed training, and seamless combination of pruning, sparsity, and quantization algorithms. NNCF allows exporting compressed models to ONNX or TensorFlow formats for use with OpenVINO™ toolkit, and supports Accuracy-Aware model training pipelines via Adaptive Compression Level Training and Early Exit Training.

The Keras-llm-robot Web UI project is an open-source tool designed for offline deployment and testing of various open-source models from the Hugging Face website. It allows users to combine multiple models through configuration to achieve functionalities like multimodal, RAG, Agent, and more. The project consists of three main interfaces: chat interface for language models, configuration interface for loading models, and tools & agent interface for auxiliary models. Users can interact with the language model through text, voice, and image inputs, and the tool supports features like model loading, quantization, fine-tuning, role-playing, code interpretation, speech recognition, image recognition, network search engine, and function calling.

GPTQModel is an easy-to-use LLM quantization and inference toolkit based on the GPTQ algorithm. It provides support for weight-only quantization and offers features such as dynamic per layer/module flexible quantization, sharding support, and auto-heal quantization errors. The toolkit aims to ensure inference compatibility with HF Transformers, vLLM, and SGLang. It offers various model supports, faster quant inference, better quality quants, and security features like hash check of model weights. GPTQModel also focuses on faster quantization, improved quant quality as measured by PPL, and backports bug fixes from AutoGPTQ.

This repository contains convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language (that isn't English). The rationale behind this is that LLaMa3 is trained on primarily English data and while it works to some extent for other languages, its performance is poor compared to English.

LLaMa2lang is a repository containing convenience scripts to finetune LLaMa3-8B (or any other foundation model) for chat towards any language that isn't English. The repository aims to improve the performance of LLaMa3 for non-English languages by combining fine-tuning with RAG. Users can translate datasets, extract threads, turn threads into prompts, and finetune models using QLoRA and PEFT. Additionally, the repository supports translation models like OPUS, M2M, MADLAD, and base datasets like OASST1 and OASST2. The process involves loading datasets, translating them, combining checkpoints, and running inference using the newly trained model. The repository also provides benchmarking scripts to choose the right translation model for a target language.

ChatGLM.cpp is a C++ implementation of ChatGLM-6B, ChatGLM2-6B, ChatGLM3-6B and more LLMs for real-time chatting on your MacBook. It is based on ggml, working in the same way as llama.cpp. ChatGLM.cpp features accelerated memory-efficient CPU inference with int4/int8 quantization, optimized KV cache and parallel computing. It also supports P-Tuning v2 and LoRA finetuned models, streaming generation with typewriter effect, Python binding, web demo, api servers and more possibilities.

LLM-Pruner is a tool for structural pruning of large language models, allowing task-agnostic compression while retaining multi-task solving ability. It supports automatic structural pruning of various LLMs with minimal human effort. The tool is efficient, requiring only 3 minutes for pruning and 3 hours for post-training. Supported LLMs include Llama-3.1, Llama-3, Llama-2, LLaMA, BLOOM, Vicuna, and Baichuan. Updates include support for new LLMs like GQA and BLOOM, as well as fine-tuning results achieving high accuracy. The tool provides step-by-step instructions for pruning, post-training, and evaluation, along with a Gradio interface for text generation. Limitations include issues with generating repetitive or nonsensical tokens in compressed models and manual operations for certain models.

The worker-vLLM repository provides a serverless endpoint for deploying OpenAI-compatible vLLM models with blazing-fast performance. It supports deploying various model architectures, such as Aquila, Baichuan, BLOOM, ChatGLM, Command-R, DBRX, DeciLM, Falcon, Gemma, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, Jais, LLaMA, MiniCPM, Mistral, Mixtral, MPT, OLMo, OPT, Orion, Phi, Phi-3, Qwen, Qwen2, Qwen2MoE, StableLM, Starcoder2, Xverse, and Yi. Users can deploy models using pre-built Docker images or build custom images with specified arguments. The repository also supports OpenAI compatibility for chat completions, completions, and models, with customizable input parameters. Users can modify their OpenAI codebase to use the deployed vLLM worker and access a list of available models for deployment.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

A curated list of Generative AI projects, tools, artworks, and models

LLMFlex is a python package designed for developing AI applications with local Large Language Models (LLMs). It provides classes to load LLM models, embedding models, and vector databases to create AI-powered solutions with prompt engineering and RAG techniques. The package supports multiple LLMs with different generation configurations, embedding toolkits, vector databases, chat memories, prompt templates, custom tools, and a chatbot frontend interface. Users can easily create LLMs, load embeddings toolkit, use tools, chat with models in a Streamlit web app, and serve an OpenAI API with a GGUF model. LLMFlex aims to offer a simple interface for developers to work with LLMs and build private AI solutions using local resources.

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