ColossalAI
label-studio
ColossalAI | label-studio | |
---|---|---|
42 | 50 | |
37,911 | 16,546 | |
1.2% | 2.5% | |
9.7 | 9.8 | |
6 days ago | 5 days ago | |
Python | JavaScript | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ColossalAI
- FLaNK AI-April 22, 2024
- Making large AI models cheaper, faster and more accessible
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ColossalChat: An Open-Source Solution for Cloning ChatGPT with a RLHF Pipeline
> open-source a complete RLHF pipeline ... based on the LLaMA pre-trained model
I've gotten to where when I see "open source AI" I now know it's "well, except for $some_other_dependencies"
Anyway: https://scribe.rip/@yangyou_berkeley/colossalchat-an-open-so... and https://github.com/hpcaitech/ColossalAI#readme (Apache 2) can save you some medium.com heartache at least
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Meet ColossalChat: An Open-Source AI Solution For Cloning ChatGPT With A Complete RLHF Pipeline
Quick Read: https://www.marktechpost.com/2023/04/01/meet-colossalchat-an-open-source-ai-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline/ Github: https://github.com/hpcaitech/ColossalAI Examples: https://chat.colossalai.org/
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A top AI researcher reportedly left Google for OpenAI after sharing concerns the company was training Bard on ChatGPT data
One of the current methods for training competing models is to have ChatGPT literally create prompt -> completion data sets. That's what was used for https://github.com/hpcaitech/ColossalAI. A model based off of the Llama weights released by facebook, then fine tuned on ChatGPT3.5 prompt + completions. So yes, there is a good chance that google is literally using ChatGPT in the training loop.
- Colossal-AI: open-source RLHF pipeline based on LLaMA pre-trained model
- ColossalChat
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ColossalChat: An Open-Source Solution for Cloning ChatGPT with RLHF Pipeline
Here's the github from the article:
https://github.com/hpcaitech/ColossalAI
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Open source solution replicates ChatGPT training process
The article talks about their RLHF implementation briefly. There’s details on their RLHF implementation here: https://github.com/hpcaitech/ColossalAI/blob/a619a190df71ea3...
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how can I make my own chatGPT?
Here’s the project on GitHub: https://github.com/hpcaitech/ColossalAI
label-studio
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Annotation is dead
If instead you have a cohort on hand — -i.e., you do not want to send your data to a third party for any reason, or perhaps you have energetic undergrads — -then you could alternatively consider local, open-source annotation such as CVAT and Label Studio. Finally, nowadays, you might instead work with Large Multimodal Models to have them annotate your data; more on this awkward angle later.
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First 15 Open Source Advent projects
14. LabelStudio by Human Signal | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
For instance, the COCO Annotator is a web-based image annotation tool tailored for the COCO dataset format, allowing collaborative labeling with features like attribute tagging and automatic segmentation. Similarly, Label Studio offers an easy-to-use interface for bounding box object labeling in images.
- FLaNK Stack Weekly for 14 Aug 2023
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You Can't Have a Free Software AI Stack
Huh?
I wrote my own system for classifying a stream of texts in Python, I might Open Source it one of these days but I have to get it to the point where it is modular enough that I can customize it to do the particular things I want without subjecting people to my whims... I use it every day and I'm not afraid to demo it because it is rock solid.
My understanding is that my system would not be hard to adapt to work on images for certain kinds of tasks.
Pytorch is open source, Huggingface is open source. CUDA isn't. This is
https://labelstud.io/
and for annotating text spans there are so many open source tools
https://github.com/doccano/doccano
I worked for a company a few years back that built annotation tools for projects we sold to customers but never quite got to a polished general purpose annotator. Today there are an overwhelming number of companies in this space and products I never heard of, many of which are cloud based or paid. Looks like a gold rush to me.
- Label Studio: Open-Source Data Labeling Platform
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Best (quickest) way to annotate images for whole-image classification?
LabelStudio is free for single use. https://labelstud.io/
- Label Studio – Free multi-type data ML labeling and annotation tool
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Way to label yolov7 images fast
LabelStudio is pretty nice, and free & open source, but I have yet to try out their ML integration with a YOLO object detection model.
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image labeling online Tools
Label Studio is an open source data labeling tool that includes annotation functionality. It provides a simple user interface (UI) that lets you label various data types, including text, audio, time series data, videos, and images, and export the information to various model formats.
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/cvat-ai/cvat]
Megatron-LM - Ongoing research training transformer models at scale
doccano - Open source annotation tool for machine learning practitioners.
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
awesome-data-labeling - A curated list of awesome data labeling tools
fairscale - PyTorch extensions for high performance and large scale training.
diffgram - The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.
DeepFaceLive - Real-time face swap for PC streaming or video calls
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
labelbox-custom-labeling-apps - Explore example custom labeling apps built with Labelbox SDK