diff --git a/README-zh-Hans.md b/README-zh-Hans.md index 4b0ba9c42..e16db47f9 100644 --- a/README-zh-Hans.md +++ b/README-zh-Hans.md @@ -3,7 +3,7 @@ [![logo](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/colossal-ai_logo_vertical.png)](https://www.colossalai.org/) - Colossal-AI: 一个面向大模型时代的通用深度学习系统 + Colossal-AI: 让AI大模型更低成本、方便易用、高效扩展

论文 | 文档 | @@ -23,10 +23,10 @@ ## 新闻 +* [2023/02] [Open source solution replicates ChatGPT training process! Ready to go with only 1.6GB GPU memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) * [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://www.hpc-ai.tech/blog/colossal-ai-0-2-0) * [2022/11] [Diffusion Pretraining and Hardware Fine-Tuning Can Be Almost 7X Cheaper](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) * [2022/10] [Use a Laptop to Analyze 90% of Proteins, With a Single-GPU Inference Sequence Exceeding 10,000](https://www.hpc-ai.tech/blog/use-a-laptop-to-analyze-90-of-proteins-with-a-single-gpu-inference-sequence-exceeding) -* [2022/10] [Embedding Training With 1% GPU Memory and 100 Times Less Budget for Super-Large Recommendation Model](https://www.hpc-ai.tech/blog/embedding-training-with-1-gpu-memory-and-10-times-less-budget-an-open-source-solution-for) * [2022/09] [HPC-AI Tech Completes $6 Million Seed and Angel Round Fundraising](https://www.hpc-ai.tech/blog/hpc-ai-tech-completes-6-million-seed-and-angel-round-fundraising-led-by-bluerun-ventures-in-the) @@ -64,6 +64,7 @@
  • Colossal-AI 成功案例 @@ -209,6 +210,29 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的

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    ## Colossal-AI 成功案例 +### ChatGPT +低成本复现[ChatGPT](https://openai.com/blog/chatgpt/)完整流程 [[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/ChatGPT) [[博客]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) +

    + +

    + +- 最高可提升单机训练速度7.73倍，单卡推理速度1.42倍 + +

    + +

    + +- 单卡模型容量最多提升10.3倍 +- 最小demo训练流程最低仅需1.62GB显存 (任意消费级GPU) + +

    + +

    + +- 提升单卡的微调模型容量3.7倍 +- 同时保持高速运行 + +

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    ### AIGC 加速AIGC(AI内容生成)模型，如[Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) 和 [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion) diff --git a/README.md b/README.md index 703e3f3bf..e4ffca890 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![logo](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/colossal-ai_logo_vertical.png)](https://www.colossalai.org/) - Colossal-AI: A Unified Deep Learning System for Big Model Era + Colossal-AI: Make big AI models cheaper, easier, and scalable

    Paper | Documentation | @@ -24,10 +24,10 @@ ## Latest News +* [2023/02] [Open source solution replicates ChatGPT training process! Ready to go with only 1.6GB GPU memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) * [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://www.hpc-ai.tech/blog/colossal-ai-0-2-0) * [2022/11] [Diffusion Pretraining and Hardware Fine-Tuning Can Be Almost 7X Cheaper](https://www.hpc-ai.tech/blog/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper) * [2022/10] [Use a Laptop to Analyze 90% of Proteins, With a Single-GPU Inference Sequence Exceeding 10,000](https://www.hpc-ai.tech/blog/use-a-laptop-to-analyze-90-of-proteins-with-a-single-gpu-inference-sequence-exceeding) -* [2022/10] [Embedding Training With 1% GPU Memory and 100 Times Less Budget for Super-Large Recommendation Model](https://www.hpc-ai.tech/blog/embedding-training-with-1-gpu-memory-and-10-times-less-budget-an-open-source-solution-for) * [2022/09] [HPC-AI Tech Completes $6 Million Seed and Angel Round Fundraising](https://www.hpc-ai.tech/blog/hpc-ai-tech-completes-6-million-seed-and-angel-round-fundraising-led-by-bluerun-ventures-in-the) ## Table of Contents @@ -64,6 +64,7 @@
  • Colossal-AI for Real World Applications @@ -211,6 +212,30 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt

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    ## Colossal-AI in the Real World +### ChatGPT +A low-cost [ChatGPT](https://openai.com/blog/chatgpt/) equivalent implementation process. [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/ChatGPT) [[blog]](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt) +

    + +

    + +- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference + +

    + +

    + +- Up to 10.3x growth in model capacity on one GPU +- A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU) + +

    + +

    + +- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU +- Keep in a sufficiently high running speed + +

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    + ### AIGC Acceleration of AIGC (AI-Generated Content) models such as [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion) and [Stable Diffusion v2](https://github.com/Stability-AI/stablediffusion).