MiniGPT-4
LangChain
Hugging Face
Haystack NLP Framework
Vercel AI SDK
Humanloop
StableLM
openplayground
Deployment.io
Harness
Jenkins
Render UIKit
GitHub Actions
Heroku
Vercel
Section.io
Deployment simplifies continuous code integration and delivery automation for startups and agile engineering teams on the AWS cloud, eliminating the need for DevOps engineering. A developer can deploy static sites, web services, and environments without knowledge of AWS or DevOps. Deployment supports previews on pull requests and automatic deployments on code push without manual setup or scripting. It enables engineering teams to focus on tasks that add customer value instead of worrying about DevOps-related grunt work.
MiniGPT-4 Deployment.ioNo features have been listed yet.
Deployment.io's answer:
I led engineering teams at early-stage startups and realized that startups waste 70% of valuable engineering time on tedious, non-coding tasks that they can easily automate.
To solve this problem, we've built Deployment.io so engineering teams at startups can focus on writing more code that adds value and helps them achieve PMF faster.
Deployment.io's answer:
ReactJs using Typescript, GatsbyJs using Typescript, GoLang, and AWS
Deployment.io's answer:
Deployment.io is built and designed for startups. Our customers can onboard in 5 minutes and start deploying apps to AWS without any DevOps or AWS knowledge. Other platforms are complex and require scripting or DevOps knowledge. They are built for bigger companies with a lot of resources.
Deployment.io's answer:
Startups and agile engineering teams should choose Deployment.io for the simplicity and ease of use. Our competitors are complex and are designed for bigger companies.
Deployment.io's answer:
For startups, speed and focus are crucial. Our primary audience is engineering teams at startups that want to focus on building code that adds value and not on DevOps related grunt work.
Deploying web apps on AWS has never been this easy and it also takes care of scaling based on usage.
Based on our record, MiniGPT-4 seems to be more popular. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Isn't there only two open multimodal LLMs, LLaVA and mini-gpt4? Source: 12 months ago
So we use MiniGPT-4 for image parsing, and yep it does return a pretty detailed (albeit not always accurate) description of the photo. You can actually play around with it on Huggingface here. Source: about 1 year ago
We use MiniGPT-4 first to interpret the image and then pass the results onto GPT-4. Hopefully, once GPT-4 makes its multi-modal functionality available, we can do it all in one request. Source: about 1 year ago
But I would like to bring up that there are some multi models(llava, miniGPT-4) that are built based on censored llama based models like vicuna. I tried several multi modal models like llava, minigpt4 and blip2. Llava has very good captioning and question answering abilities and it is also much faster than the others(basically real time), though it has some hallucination issue. Source: about 1 year ago
Https://minigpt-4.github.io/ <-- free image recognition, although not powered by true GPT-4. Source: about 1 year ago
LangChain - Framework for building applications with LLMs through composability
Harness - Automated Tests For Your Web App
Hugging Face - The Tamagotchi powered by Artificial Intelligence 🤗
Jenkins - Jenkins is an open-source continuous integration server with 300+ plugins to support all kinds of software development
Haystack NLP Framework - Haystack is an open source NLP framework to build applications with Transformer models and LLMs.
Render UIKit - React-inspired Swift library for writing UIKit UIs