Skip to main content

Andromeda - Pytorch

Project description

Multi-Modality

Andromeda: Ultra-Fast and Ultra-Intelligent SOTA Language Model 🚀🌌

Open Bounties Rewarded Bounties GitHub issues GitHub forks GitHub stars GitHub license Share on Twitter Share on Facebook Share on LinkedIn Discord Share on Reddit Share on Hacker News Share on Pinterest Share on WhatsApp

Welcome to Andromeda, The Fastest, Most Creative, and Reliable Language Model Ever Built, train your own verison, conduct inference, and finetune your own verison with simple plug in and play scripts get started in 10 seconds:

Features

  • 💼 Handle Ultra Long Sequences (32,000-200,000+ context lengths)
  • ⚡ Ultra Fast Processing (32,000+ tokens in under 100ms)
  • 🎓 Superior Reasoning Capabilities

🎯 Principles

  • Efficiency: Optimize with techniques like attention flashing, rotary position encodings, and deep normalization.
  • Flexibility: Adapt to various tasks and domains for wide applications.
  • Scalability: Designed to scale with resources and data sizes.
  • Community-Driven: Thrives on contributions from the open-source community.

💻 Install

pip install TheBestLLMEver

Usage

import torch

from andromeda.configs import Andromeda1Billion

model = Andromeda1Billion()

x = torch.randint(0, 256, (1, 1024)).cuda()

out = model(x)  # (1, 1024, 20000)
print(out)

📚 Training

  1. Set the environment variables:

    • ENTITY_NAME: Your wandb project name
    • OUTPUT_DIR: Directory to save the weights (e.g., ./weights)
    • MASTER_ADDR: For distributed training
    • MASTER_PORT For master port distributed training
    • RANK- Number of nodes services
    • WORLD_SIZE Number of gpus
  2. Configure the training:

    • Accelerate Config
    • Enable Deepspeed 3
    • Accelerate launch train_distributed_accelerate.py

For more information, refer to the Training SOP.


Todo


📈 Benchmarks

Speed

  • Andromeda utilizes one of the most reliable Attentions ever, flash attention 2.0 Triton. It consumes 50x less memory than GPT-3 and 10x less than LLAMA.

AndromedaBanner

  • We can speed this up even more with dynamic sparse flash attention 2.0.

🔮 Join the Journey

We're just getting started, and we invite you to join the journey. Let's revolutionize the NLP landscape together! 🚀🌟

  • Join Agora and work with 2,000+ AI Engineers to implement all new features.
  • Provide compute and help train Andromeda.
  • Share the message on how we're liberating this superintelligent AI and seizing the power from the corrupt, providing it back to you.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

andromeda_torch-0.0.4.tar.gz (96.7 kB view details)

Uploaded Source

Built Distribution

andromeda_torch-0.0.4-py3-none-any.whl (56.0 kB view details)

Uploaded Python 3

File details

Details for the file andromeda_torch-0.0.4.tar.gz.

File metadata

  • Download URL: andromeda_torch-0.0.4.tar.gz
  • Upload date:
  • Size: 96.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.11.0 Darwin/22.4.0

File hashes

Hashes for andromeda_torch-0.0.4.tar.gz
Algorithm Hash digest
SHA256 ad1199fdb392bc74c081508234486f3796ccd3596f3de4e5a5182fb1ac500906
MD5 535e5de710ba0e7add38fd4ccfeeb355
BLAKE2b-256 fcb5508b64ea26c010442c4861dd33837a3ff0fd945f08c9642cf130ca4d593d

See more details on using hashes here.

File details

Details for the file andromeda_torch-0.0.4-py3-none-any.whl.

File metadata

  • Download URL: andromeda_torch-0.0.4-py3-none-any.whl
  • Upload date:
  • Size: 56.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.3.2 CPython/3.11.0 Darwin/22.4.0

File hashes

Hashes for andromeda_torch-0.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 d7ac052278776301594a406e2cf762a748fa5dcc3e265991c9419770624fdc87
MD5 e76bb27903e3279ed81ff52058c2c52d
BLAKE2b-256 e2822dff0d3e8e398ec3ee4853e7dd81f379c91c86e9f60b2d31aa96c718ae2a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page