An LLM framework
Project description
🧠 Geniusrise
About
Geniusrise is a modular, loosely-coupled AgentOps / MLOps framework designed for the era of Large Language Models, offering flexibility, inclusivity, and standardization in designing networks of AI agents.
It seamlessly integrates tasks, state management, data handling, and model versioning, all while supporting diverse infrastructures and user expertise levels. With its plug-and-play architecture, Geniusrise empowers teams to build, share, and deploy AI agent workflows across various platforms efficiently.
TLDR 🙄
1. Install geniusrise
pip install geniusrise
pip install geniusrise-huggingface
2. Create genius.yaml
version: '1'
bolts:
HuggingFaceInstructionTuningBolt:
name: 'hf-fine-tune-my-shit'
method: fine_tune
args:
model_name: bert-base-uncased
tokenizer_name: bert-base-uncased
batches: 2
hf_repo_id: my/repo
token: 'hf_woohoo'
commit_message: say hello to genius!
input:
type: batch
args:
bucket: geniusrise-test
folder: my-shit
output:
type: batch
args:
bucket: geniusrise-test
folder: my-model
deploy:
type: 'k8s'
args:
cluster_name: my-cluster
namespace: geniusrise-huggingface
labels: { 'needs': 'gpu' }
cpu: 16
memory: 50G
storage: 250Gb
gpu: 1
3. Copy data to s3
cat > data.jsonl <<- EOM
{"instruction": "instruction1", "output":"output1"}
{"instruction": "instruction2", "output":"output2"}
EOM
aws s3 cp data.jsonl s3://geniusrise-test/my-shit/
4. Fine tune
genius --yaml genius.yaml deploy
🙄 This was not even crux of the iceberg. Please see docs.
Links
- Website: geniusrise.ai
- Docs: docs.geniusrise.ai
- Hub: geniusrise.com [coming soon]
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