Best AI tools for< Hang A Picture Frame >

BeautyPlus is an AI photo editor and design tool online platform that offers a wide range of features to enhance photos and videos. It provides creative AI-powered tools for editing images and videos, including an AI video enhancer, image enhancer, photo collage templates, avatar generator, face editor, and intuitive photo & video editing tools. With BeautyPlus, users can transform their photos and videos with stunning effects and professional-looking results. The platform is available on iOS, Android, and browser-based, making it accessible to a wide range of users.

Artsire is a revolutionary platform that offers a unique wall art shopping experience. Users can design custom artworks using the AI generator or explore a vast gallery of ready-to-hang prints. The platform provides premium quality prints on eco-friendly materials with ultra-fast shipping across the United States. Artsire aims to transform users' imagination into reality by offering a seamless and hassle-free art experience.

Bitrix24 is an all-in-one online workspace for businesses, offering features such as CRM, task management, online meetings, and more. It provides tools for managing sales, customer relationships, communication, collaboration, marketing, project management, human resources, and automation. Bitrix24 aims to streamline workflows, enhance team collaboration, and improve productivity by integrating various business functions into a single platform.

Zeus Notebook is an AI code assistant designed by Ying Hang Seah. It allows users to run a Python notebook entirely on their browser. Users can enter their OpenAI API key to enable chat functionality. The application is a helpful tool for developers and programmers to get assistance with coding tasks and projects.

VideoTuna is a codebase for text-to-video applications that integrates multiple AI video generation models for text-to-video, image-to-video, and text-to-image generation. It provides comprehensive pipelines in video generation, including pre-training, continuous training, post-training, and fine-tuning. The models in VideoTuna include U-Net and DiT architectures for visual generation tasks, with upcoming releases of a new 3D video VAE and a controllable facial video generation model.

InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

BetterGI is a project based on computer vision technology, which aims to make Genshin Impact better. It can automatically pick up items, skip dialogues, automatically select options, automatically submit items, close pop-up pages, etc. When talking to Katherine, it can automatically receive the "Daily Commission" rewards and automatically re-dispatch. When the automatic plot function is turned on, this function will take effect, and the invitation options will be automatically selected. AI recognizes automatic casting, automatically reels in when the fish is hooked, and automatically completes the fishing progress. Help you easily complete the Seven Saint Summoning character invitation, weekly visitor challenge and other PVE content. Automatically use the "King Tree Blessing" with the `Z` key, and use the principle of refreshing wood by going online and offline to hang up a backpack full of wood. Write combat scripts to let the team fight automatically according to your strategy. Fully automatic secret realm hangs up to restore physical strength, automatically enters the secret realm to open the key, fight, walk to the ancient tree and receive rewards. Click the teleportation point on the map, or if there is a teleportation point in the list that appears after clicking, it will automatically click the teleportation point and teleport. Set a shortcut key, and long press to continuously rotate the perspective horizontally (of course you can also use it to rotate the grass god). Quickly switch between "Details" and "Enhance" pages to skip the display of holy relic enhancement results and quickly +20. You can quickly purchase items in the store in full quantity, which is suitable for quickly clearing event redemptions,塵歌壺 store redemptions, etc.

A-MEM is a novel agentic memory system designed for Large Language Model (LLM) agents to dynamically organize memories in an agentic way. It introduces advanced memory organization capabilities, intelligent indexing, and linking of memories, comprehensive note generation, interconnected knowledge networks, continuous memory evolution, and agent-driven decision making for adaptive memory management. The system facilitates agent construction and enables dynamic memory operations and flexible agent-memory interactions.

ChaiNNer is a node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. It gives users a high level of control over their processing pipeline and allows them to perform complex tasks by connecting nodes together. ChaiNNer is cross-platform, supporting Windows, MacOS, and Linux. It features an intuitive drag-and-drop interface, making it easy to create and modify processing chains. Additionally, ChaiNNer offers a wide range of nodes for various image processing tasks, including upscaling, denoising, sharpening, and color correction. It also supports batch processing, allowing users to process multiple images or videos at once.

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

Gemini AI is a Ruby Gem designed to provide low-level access to Google's generative AI services through Vertex AI, Generative Language API, or AI Studio. It allows users to interact with Gemini to build abstractions on top of it. The Gem provides functionalities for tasks such as generating content, embeddings, predictions, and more. It supports streaming capabilities, server-sent events, safety settings, system instructions, JSON format responses, and tools (functions) calling. The Gem also includes error handling, development setup, publishing to RubyGems, updating the README, and references to resources for further learning.

Ollama AI is a Ruby gem designed to interact with Ollama's API, allowing users to run open source AI LLMs (Large Language Models) locally. The gem provides low-level access to Ollama, enabling users to build abstractions on top of it. It offers methods for generating completions, chat interactions, embeddings, creating and managing models, and more. Users can also work with text and image data, utilize Server-Sent Events for streaming capabilities, and handle errors effectively. Ollama AI is not an official Ollama project and is distributed under the MIT License.

MaxText is a high-performance, highly scalable, open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference. MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler. MaxText aims to be a launching off point for ambitious LLM projects both in research and production. We encourage users to start by experimenting with MaxText out of the box and then fork and modify MaxText to meet their needs.

MetaVoice-1B is a 1.2B parameter base model trained on 100K hours of speech for TTS (text-to-speech). It has been built with the following priorities: * Emotional speech rhythm and tone in English. * Zero-shot cloning for American & British voices, with 30s reference audio. * Support for (cross-lingual) voice cloning with finetuning. * We have had success with as little as 1 minute training data for Indian speakers. * Synthesis of arbitrary length text

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

SeaLLMs are a family of language models optimized for Southeast Asian (SEA) languages. They were pre-trained from Llama-2, on a tailored publicly-available dataset, which comprises texts in Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. The SeaLLM-chat underwent supervised finetuning (SFT) and specialized self-preferencing DPO using a mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which **adapt to the local cultural norms, customs, styles and laws in these areas**. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform **ChatGPT-3.5** in non-Latin languages, such as Thai, Khmer, Lao, and Burmese.

TaskWeaver is a code-first agent framework designed for planning and executing data analytics tasks. It interprets user requests through code snippets, coordinates various plugins to execute tasks in a stateful manner, and preserves both chat history and code execution history. It supports rich data structures, customized algorithms, domain-specific knowledge incorporation, stateful execution, code verification, easy debugging, security considerations, and easy extension. TaskWeaver is easy to use with CLI and WebUI support, and it can be integrated as a library. It offers detailed documentation, demo examples, and citation guidelines.

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.

VideoLLaMA 2 is a project focused on advancing spatial-temporal modeling and audio understanding in video-LLMs. It provides tools for multi-choice video QA, open-ended video QA, and video captioning. The project offers model zoo with different configurations for visual encoder and language decoder. It includes training and evaluation guides, as well as inference capabilities for video and image processing. The project also features a demo setup for running a video-based Large Language Model web demonstration.

EmbodiedScan is a holistic multi-modal 3D perception suite designed for embodied AI. It introduces a multi-modal, ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding. The dataset includes over 5k scans with 1M ego-centric RGB-D views, 1M language prompts, 160k 3D-oriented boxes spanning 760 categories, and dense semantic occupancy with 80 common categories. The suite includes a baseline framework named Embodied Perceptron, capable of processing multi-modal inputs for 3D perception tasks and language-grounded tasks.

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