magnet

📖 docs | 💻 examples | 📓 substack

the small distributed language model toolkit

⚡️ fine-tune state-of-the-art LLMs anywhere, rapidly ⚡️

🧬 Installation

pip install llm-magnet

or

python3 setup.py install

🎉 usage

check out the example notebooks

a snippet to get you started

from magnet.base import Magnet
from magnet.base import EmbeddedMagnet

cluster = EmbeddedMagnet()
cluster.start()
magnet = cluster.create_magnet()
await magnet.align()

config = {
    "host": "127.0.0.1",
    "credentials": None,
    "domain": None,
    "name": "my_stream",
    "category": "my_category",
    "kv_name": "my_kv",
    "session": "my_session",
    "os_name": "my_object_store",
    "index": {
        "milvus_uri": "127.0.0.1",
        "milvus_port": 19530,
        "milvus_user": "test",
        "milvus_password": "test",
        "dimension": 1024,
        "model": "BAAI/bge-large-en-v1.5",
        "name": "test",
        "options": {
            'metric_type': 'COSINE',
            'index_type':'HNSW',
            'params': {
                "efConstruction": 40
                , "M": 48
            }
        }
    }
}

magnet = Magnet(config)
await magnet.align()

🔮 features

• ⚡️ It's Fast
• fast on consumer hardware
• very fast on Apple Silicon
• extremely fast on ROCm/CUDA
• 🫵 Automatic or your way
• rely on established transformer patterns to let magnet do the work
• keep your existing data processing functions, bring them to magnet!
• 🛰️ 100% Distributed
• processing, embedding, storage, retrieval, querying, or inference from anywhere
• as much or as little compute as you need
• 🧮 Choose Inference Method
• HuggingFace
• vLLM node
• GPU
• mlx
• 🌎 Huge Volumes
• handle gigantic amounts of data inexpensively
• fault-tolerant by design
• decentralized workloads
• 🔐 Secure
• JWT
• Basic
• 🪵 World-Class Comprehension
• magnet optionally logs its own code as it's executed (yes, really)
• build a self-aware system and allow it to learn from itself
• emojis are the future

🧲 why

• build a distributed LLM research node with any hardware, from Rasbperry Pi to the expensive cloud
• Apple silicon first-class citizen with mlx
• embed & index to vector db with milvus
• distributed processing with NATS
• upload to S3
• ideal cyberpunk vision of LLM power users in vectorspace