Multi-Tenancy Training

Twinkle supports simultaneous multi-tenant training on a shared base model, dramatically reducing deployment costs while enabling flexible configurations per tenant.

Key Features

• Resource Efficiency: Single base model serves multiple concurrent training sessions
• Complete Isolation: Each tenant has separate LoRA weights, optimizers, and loss functions
• Heterogeneous Configs: Different ranks, learning rates, and training objectives per tenant
• Concurrent Access: No interference between training sessions

Use Cases

Example

from twinkle_client import init_twinkle_client
from twinkle_client.model import MultiLoraTransformersModel

client = init_twinkle_client(base_url='http://server:8000')

model = MultiLoraTransformersModel(model_id='ms://Qwen/Qwen3.5-4B')
model.add_adapter_to_model('tenant_a', LoraConfig(r=8))
model.set_loss('GRPOLoss', epsilon=0.2)

for batch in dataloader:
    model.forward_backward(inputs=batch)
    model.step()