Best AI tools for< Sailor >
Infographic
1 - AI tool Sites

SEA.AI
SEA.AI is an AI tool that provides Machine Vision for Safety at Sea. It utilizes the latest camera technology combined with artificial intelligence to detect and classify objects on the surface of the water, including unsignalled craft, floating obstacles, buoys, kayaks, and persons overboard. The application offers various solutions for sailing, commercial, motor, maritime surveillance, search & rescue, and government sectors. SEA.AI aims to enhance safety and convenience for sailors by leveraging AI technology for early detection of potential hazards at sea.
8 - Open Source Tools

sailor-llm
Sailor is a suite of open language models tailored for South-East Asia (SEA), focusing on languages such as Indonesian, Thai, Vietnamese, Malay, and Lao. Developed with careful data curation, Sailor models are designed to understand and generate text across diverse linguistic landscapes of the SEA region. Built from Qwen 1.5, Sailor encompasses models of varying sizes, spanning from 0.5B to 7B versions for different requirements. Benchmarking results demonstrate Sailor's proficiency in tasks such as question answering, commonsense reasoning, reading comprehension, and more in SEA languages.

Awesome-Model-Merging-Methods-Theories-Applications
A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

llm-continual-learning-survey
This repository is an updating survey for Continual Learning of Large Language Models (CL-LLMs), providing a comprehensive overview of various aspects related to the continual learning of large language models. It covers topics such as continual pre-training, domain-adaptive pre-training, continual fine-tuning, model refinement, model alignment, multimodal LLMs, and miscellaneous aspects. The survey includes a collection of relevant papers, each focusing on different areas within the field of continual learning of large language models.

awesome-hosting
awesome-hosting is a curated list of hosting services sorted by minimal plan price. It includes various categories such as Web Services Platform, Backend-as-a-Service, Lambda, Node.js, Static site hosting, WordPress hosting, VPS providers, managed databases, GPU cloud services, and LLM/Inference API providers. Each category lists multiple service providers along with details on their minimal plan, trial options, free tier availability, open-source support, and specific features. The repository aims to help users find suitable hosting solutions based on their budget and requirements.

chatgpt-universe
ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

awesome-large-audio-models
This repository is a curated list of awesome large AI models in audio signal processing, focusing on the application of large language models to audio tasks. It includes survey papers, popular large audio models, automatic speech recognition, neural speech synthesis, speech translation, other speech applications, large audio models in music, and audio datasets. The repository aims to provide a comprehensive overview of recent advancements and challenges in applying large language models to audio signal processing, showcasing the efficacy of transformer-based architectures in various audio tasks.

awesome_LLM-harmful-fine-tuning-papers
This repository is a comprehensive survey of harmful fine-tuning attacks and defenses for large language models (LLMs). It provides a curated list of must-read papers on the topic, covering various aspects such as alignment stage defenses, fine-tuning stage defenses, post-fine-tuning stage defenses, mechanical studies, benchmarks, and attacks/defenses for federated fine-tuning. The repository aims to keep researchers updated on the latest developments in the field and offers insights into the vulnerabilities and safeguards related to fine-tuning LLMs.