An LLM framework
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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.
TLDR 🙄
1. Install geniusrise
pip install geniusrise
pip install geniusrise-huggingface
2. Create genius.yaml
version: '1'
bolts:
http_classifier:
name: HuggingFaceClassificationFineTuner
method: fine_tune
args:
model_name: bert-base-uncased
tokenizer_name: bert-base-uncased
num_train_epochs: 2
per_device_train_batch_size: 2
model_class: BertForSequenceClassification
tokenizer_class: BertTokenizer
data_masked: true
hf_repo_id: your/hf_model_repo
hf_commit_message: 'say hello to geniusrise!'
hf_create_pr: true
hf_token: hf_mykey
state:
type: none
input:
type: batch
args:
bucket: my-s3-bucket
folder: training_data
output:
type: batch
args:
bucket: my-s3-bucket
folder: model
deploy:
type: k8s
args:
kind: deployment
name: classifier
context_name: arn:aws:eks:us-east-1:genius-dev:cluster/geniusrise-dev
namespace: geniusrise
image: geniusrise/geniusrise
kube_config_path: ~/.kube/config
cpu: 16
memory: 50G
storage: 250Gb
gpu: 1
3. Deploy
genius rise up
🙄 This was not even tip of the iceberg. Please see docs.
Links
- Website: geniusrise.ai
- Docs: docs.geniusrise.ai
- Hub: geniusrise.com [coming soon]
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