Models

# Basic listing
models = client.models.list("run_abc123")

for model in models.items:
    print(f"Model: {model.model_id}")
    print(f"Epoch: {model.spec.epoch}")

# With custom pagination
from vi.api.types import PaginationParams

models = client.models.list(
    run_id_or_link="run_abc123",
    pagination=PaginationParams(page_size=50)
)

for model in models.items:
    print(f"{model.model_id}")

# Using dict for pagination
models = client.models.list(
    run_id_or_link="run_abc123",
    pagination={"page_size": 50}
)

# List with metrics
models = client.models.list("run_abc123")

print("Available Models:")
for model in models.items:
    epoch = model.spec.epoch or "N/A"
    metrics_str = "No metrics"

    if model.spec.evaluation_metrics:
        metrics = model.spec.evaluation_metrics
        metrics_str = ", ".join(f"{k}: {v:.4f}" for k, v in metrics.items())

    print(f"  Epoch {epoch}: {metrics_str}")

# Find best model by metric
from vi.api.resources.models.responses import Model

def find_best_model(run_id: str, metric_name: str = "accuracy") -> tuple[Model | None, float]:
    """Find model with best metric value."""
    models = client.models.list(run_id)

    best_model: Model | None = None
    best_value = float('-inf')

    for model in models.items:
        if not model.spec.evaluation_metrics:
            continue

        value = model.spec.evaluation_metrics.get(metric_name, 0)
        if value > best_value:
            best_value = value
            best_model = model

    return best_model, best_value

best, value = find_best_model("run_abc123", "accuracy")
if best:
    print(f"Best model: {best.model_id}")
    print(f"Accuracy: {value}")

# Get latest model
model = client.models.get(run_id_or_link="run_abc123")

print(f"Model ID: {model.model_id}")
print(f"Epoch: {model.spec.epoch}")

# Get specific checkpoint
model = client.models.get(
    run_id_or_link="run_abc123",
    ckpt="epoch_10"
)

print(f"Model ID: {model.model_id}")
print(f"Epoch: {model.spec.epoch}")

# Access model metrics
model = client.models.get(run_id_or_link="run_abc123")

if model.spec.evaluation_metrics:
    for metric, value in model.spec.evaluation_metrics.items():
        print(f"{metric}: {value}")

# Display detailed information
model = client.models.get(run_id_or_link="run_abc123")
model.info()  # Prints formatted model summary

# Basic download
downloaded = client.models.download(
    run_id_or_link="run_abc123",
    save_dir="./models"
)

print(f"Model path: {downloaded.model_path}")

# Download specific checkpoint
downloaded = client.models.download(
    run_id_or_link="run_abc123",
    ckpt="epoch_10",
    save_dir="./models"
)

print(f"Model: {downloaded.model_path}")
print(f"Config: {downloaded.run_config_path}")

# Using the convenience method on client
downloaded = client.get_model(
    run_id="run_abc123",
    save_path="./models"
)

print(f"✓ Model downloaded!")
print(f"Model: {downloaded.model_path}")
print(f"Config: {downloaded.run_config_path}")

# Cache downloaded models
from pathlib import Path

def get_or_download_model(run_id: str, save_dir: str = "./models") -> dict[str, str]:
    """Download model if not already cached."""
    model_dir = Path(save_dir) / run_id

    # Check cache
    if model_dir.exists() and (model_dir / "model_full").exists():
        print(f"Using cached model at {model_dir}")
        return {
            "model_path": str(model_dir / "model_full"),
            "run_config_path": str(model_dir / "run_config.json")
        }

    # Download
    return client.get_model(run_id=run_id, save_path=save_dir)

result = get_or_download_model("run_abc123")

# Load downloaded model for inference
from vi.inference import ViModel

# Download first
downloaded = client.get_model(
    run_id="run_abc123",
    save_path="./models"
)

# Load for inference
model = ViModel(
    secret_key="your-secret-key",
    organization_id="your-organization-id",
    run_id="run_abc123"
)

# Run inference
result, error = model(
    source="test.jpg",
    user_prompt="Describe this image"
)

if error is None:
    print(result.caption)

from vi.api.resources.models.responses import Model

from vi.api.resources.models.responses import ModelSpec

from vi.api.resources.models.responses import ModelStatus

from vi.api.resources.models.responses import ModelContents

from vi.api.resources.models.responses import ModelDownloadUrl