Saving and Loading

MinT provides flexible methods for saving and loading model weights and optimizer states.

Save Methods

Save Weights for Sampler

Stores weights optimized for inference (faster):

sampling_path = training_client.save_weights_for_sampler(name="0000").result().path

Save Full State

Preserves both weights and optimizer state for resuming training:

resume_path = training_client.save_state(name="0010").result().path

Creating a Sampling Client

import mint
service_client = mint.ServiceClient()
training_client = service_client.create_lora_training_client(
    base_model="Qwen/Qwen3-4B-Instruct-2507", rank=32
)
 
sampling_path = training_client.save_weights_for_sampler(name="0000").result().path
sampling_client = service_client.create_sampling_client(model_path=sampling_path)

Resuming Training

resume_path = training_client.save_state(name="0010").result().path
training_client.load_state(resume_path)

Use Cases

Multi-stage workflows - Save intermediate checkpoints
Hyperparameter adjustments - Resume from a checkpoint with new settings
Failure recovery - Restore training after interruptions
Optimizer state preservation - Maintain training momentum across sessions

Loss Functions Downloading Weights