Deploy models with custom weights

Preview

Deploy models with custom weights is a Preview offering, subject to the "Pre-GA Offerings Terms" of the Google Cloud Service Specific Terms. Pre-GA products and features may have limited support, and changes to Pre-GA products and features may not be compatible with other Pre-GA versions. For more information, see the launch stage descriptions. Further, by using the Gemini API on Vertex AI, you agree to the Generative AI Preview terms and conditions (Preview Terms).

Deploy models with custom weights is a Preview offering. You can fine tune models based on a predefined set of base models, and deploy your customized models on Vertex AI Model Garden. You can deploy your custom models using the custom weights import by uploading your model artifacts to a Cloud Storage bucket in your project, which is a one-click experience in Vertex AI.

Supported models

The public preview of Deploy models with custom weights is supported by the following base models:

Model name	Version
Llama	Llama-2: 7B, 13B Llama-3.1: 8B, 70B Llama-3.2: 1B, 3B Llama-4: Scout-17B, Maverick-17B CodeLlama-13B
Gemma	Gemma-2: 27B Gemma-3: 1B, 4B, 3-12B, 27B Medgemma: 4B, 27B-text
Qwen	Qwen2: 1.5B Qwen2.5: 0.5B, 1.5B, 7B, 32B Qwen3: 0.6B, 1.7B, 8B, 32B, Qwen3-Coder-480B-A35B-Instruct
Deepseek	Deepseek-R1 Deepseek-V3
Mistral and Mixtral	Mistral-7B-v0.1 Mixtral-8x7B-v0.1 Mistral-Nemo-Base-2407
Phi-4	Phi-4-reasoning
OpenAI OSS	gpt-oss: 20B, 120B

Limitations

Custom weights don't support the import of quantized models.

Model files

You must supply the model files in the Hugging Face weights format. For more information on the Hugging Face weights format, see Use Hugging Face Models.

If the required files aren't provided, the model deployment might fail.

This table lists the types of model files, which depend on the model's architecture:

Model file content	File type
Model configuration	config.json
Model weights	*.safetensors *.bin
Weights index	*.index.json
Tokenizer file(s)	tokenizer.model tokenizer.json tokenizer_config.json

Locations

You can deploy custom models in all regions from Model Garden services.

Prerequisites

This section demonstrates how to deploy your custom model.

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Enable the API

1. In the Google Cloud console, activate Cloud Shell.

   Activate Cloud Shell

   At the bottom of the Google Cloud console, a Cloud Shell session starts and displays a command-line prompt. Cloud Shell is a shell environment with the Google Cloud CLI already installed and with values already set for your current project. It can take a few seconds for the session to initialize.

This tutorial assumes that you are using Cloud Shell to interact with Google Cloud. If you want to use a different shell instead of Cloud Shell, then perform the following additional configuration:

1. Install the Google Cloud CLI.

2. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.

3. To initialize the gcloud CLI, run the following command:

   gcloud init

Deploy the custom model

This section demonstrates how to deploy your custom model.

If you're using the command-line interface (CLI), Python, or JavaScript, replace the following variables with a value for your code samples to work:

• REGION: Your region. For example, uscentral1.
• MODEL_GCS: Your Google Cloud model. For example, gs://custom-weights-fishfooding/meta-llama/Llama-3.2-1B-Instruct.
• PROJECT_ID: Your project ID.
• MODEL_ID: Your model ID.
• MACHINE_TYPE: Your machine type. For example, g2-standard-12.
• ACCELERATOR_TYPE: Your accelerator type. For example, NVIDIA_L4.
• ACCELERATOR_COUNT: Your accelerator count.

• PROMPT: Your text prompt.

Console

The following steps show you how to use the Google Cloud console to deploy your model with custom weights.

1. In the Google Cloud console, go to the Model Garden page.

   Go to Model Garden

2. Click Deploy model with custom weights. The Deploy a model with custom weights on Vertex AI pane appears.

3. In the Model source section, do the following:

   1. Click Browse, and choose your bucket where your model is stored, and click Select.

   2. Optional: Enter your model's name in the Model name field.

4. In the Deployment settings section, do the following:

   1. From the Region field, select your region, and click OK.

   2. In the Machine Spec field, select your machine specification, which is used to the deploy your model.

   3. Optional: In the Endpoint name field, your model's endpoint appears by default. However, you can enter a different endpoint name in the field.

5. Click Deploy model with custom weights.

gcloud CLI

This command demonstrates how to deploy the model to a specific region.

gcloud ai model-garden models deploy --model=${MODEL_GCS} --region ${REGION}

This command demonstrates how to deploy the model to a specific region with its machine type, accelerator type, and accelerator count. If you want to select a specific machine configuration, then you must set all three fields.

gcloud ai model-garden models deploy --model=${MODEL_GCS} --machine-type=${MACHINE_TYE} --accelerator-type=${ACCELERATOR_TYPE} --accelerator-count=${ACCELERATOR_COUNT} --region ${REGION}

Python

import vertexai
from google.cloud import aiplatform
from vertexai.preview import model_garden

vertexai.init(project=${PROJECT_ID}, location=${REGION})
custom_model = model_garden.CustomModel(
  gcs_uri=GCS_URI,
)
endpoint = custom_model.deploy(
  machine_type="${MACHINE_TYPE}",
  accelerator_type="${ACCELERATOR_TYPE}",
  accelerator_count="${ACCELERATOR_COUNT}",
  model_display_name="custom-model",
  endpoint_display_name="custom-model-endpoint")

endpoint.predict(instances=[{"prompt": "${PROMPT}"}], use_dedicated_endpoint=True)

Alternatively, you don't have to pass a parameter to the custom_model.deploy() method.

import vertexai
from google.cloud import aiplatform
from vertexai.preview import model_garden

vertexai.init(project=${PROJECT_ID}, location=${REGION})
custom_model = model_garden.CustomModel(
  gcs_uri=GCS_URI,
)
endpoint = custom_model.deploy()

endpoint.predict(instances=[{"prompt": "${PROMPT}"}], use_dedicated_endpoint=True)

curl

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  "https://${REGION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${REGION}:deploy" \
  -d '{
    "custom_model": {
    "gcs_uri": "'"${MODEL_GCS}"'"
  },
  "destination": "projects/'"${PROJECT_ID}"'/locations/'"${REGION}"'",
  "model_config": {
     "model_user_id": "'"${MODEL_ID}"'",
  },
}'

Alternatively, you can use the API to explicitly set the machine type.

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  "https://${REGION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${REGION}:deploy" \
  -d '{
    "custom_model": {
    "gcs_uri": "'"${MODEL_GCS}"'"
  },
  "destination": "projects/'"${PROJECT_ID}"'/locations/'"${REGION}"'",
  "model_config": {
     "model_user_id": "'"${MODEL_ID}"'",
  },
  "deploy_config": {
    "dedicated_resources": {
      "machine_spec": {
        "machine_type": "'"${MACHINE_TYPE}"'",
        "accelerator_type": "'"${ACCELERATOR_TYPE}"'",
        "accelerator_count": '"${ACCELERATOR_COUNT}"'
      },
      "min_replica_count": 1
    }
  }
}'

Learn more about self-deployed models in Vertex AI

• For more information about self-deployed models, see Overview of self-deployed models.
• For more information about Model Garden, see Overview of Model Garden.
• For more information about deploying models, see Use models in Model Garden.
• Use Gemma open models
• Use Llama open models
• Use Hugging Face open models