PaLM-colossalai
ColossalAI
| PaLM-colossalai | ColossalAI | |
|---|---|---|
| 2 | 42 | |
| 192 | 37,911 | |
| - | 1.2% | |
| 0.0 | 9.7 | |
| over 1 year ago | 5 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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PaLM-colossalai
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Implementing the Gargantuan Pathways with Colossal-AI, easy and efficient!
If you feel interested, do check out our GitHub repo: https://github.com/hpcaitech/PaLM-colossalai
- [P] Scalable PaLM implementation of PyTorch
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
What are some alternatives?
PaLM-pytorch - Implementation of the specific Transformer architecture from PaLM - Scaling Language Modeling with Pathways
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Megatron-LM - Ongoing research training transformer models at scale
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.
fairscale - PyTorch extensions for high performance and large scale training.
DeepFaceLive - Real-time face swap for PC streaming or video calls
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）
ivy - The Unified AI Framework
DeepFaceLab - DeepFaceLab is the leading software for creating deepfakes.
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
PaddleNLP - 👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.