Import Models

Learn how to import models into the Clarifai platform

The Clarifai platform allows you to import and integrate externally developed, pre-trained models. This feature allows you to incorporate a diverse array of advanced models into the Clarifai platform and open up new possibilities for harnessing artificial intelligence (AI) in a wide range of applications.

Once imported, the model will be deployed and ready to be evaluated, fine-tuned and customized, combined with other models and agent operators in a workflow, or used to serve inference requests as it is.

info

The import model functionality is exclusively available to Enterprise users.

Two main ways are:

Hugging Face Importation: Find and import pre-trained models from the Hugging Face platform.
Local Model Upload: Upload your own trained models if they meet Clarifai's format requirements.

info

The initialization code used in the following examples is outlined in detail on the client installation page.

Hugging Face Importation

Let’s illustrate how you could import a model from Hugging Face and deploy it on your own Clarifai app.

tip

Click here to learn how to find a model that meets Clarifai's requirements.

Zero-Shot-Text-Classifier

To import a zero-shot-text-classifier model type, you need to start by creating it.

##########################################################################################
# In this section, we set the user authentication, app ID, model ID, and model type ID.
# Change these strings to run your own example.
#########################################################################################

USER_ID = 'YOUR_USER_ID_HERE'
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = 'YOUR_PAT_HERE'
APP_ID = 'YOUR_APP_ID_HERE'
# Change these to create your own model
MODEL_ID = 'zero-shot-text-model'
MODEL_TYPE_ID = 'zero-shot-text-classifier'

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (('authorization', 'Key ' + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

post_models_response = stub.PostModels(
    service_pb2.PostModelsRequest(
        user_app_id=userDataObject,
        models=[
            resources_pb2.Model(
                id=MODEL_ID,
                model_type_id=MODEL_TYPE_ID,
                notes='zero shot text classifier'         
            )
        ]
    ),
    metadata=metadata
)

if post_models_response.status.code != status_code_pb2.SUCCESS:
    print(post_models_response.status)
    raise Exception("Post models failed, status: " + post_models_response.status.description)

<!--index.html file-->

<script>
    ///////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model type ID.
    // Change these strings to run your own example.
    //////////////////////////////////////////////////////////////////////////////////////////

    const USER_ID = 'YOUR_USER_ID_HERE';
    // Your PAT (Personal Access Token) can be found in the Account's Security section
    const PAT = 'YOUR_PAT_HERE';
    const APP_ID = 'YOUR_APP_ID_HERE';
    // Change these to create your own model
    const MODEL_ID = 'zero-shot-text-model';
    const MODEL_TYPE_ID = 'zero-shot-text-classifier';
    
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////

    const raw = JSON.stringify({
        'user_app_id': {
            'user_id': USER_ID,
            'app_id': APP_ID
        },
        'model': {
            'id': MODEL_ID,
            'model_type_id': MODEL_TYPE_ID,
            'notes': 'zero shot text classifier'       
        }
    });

    const requestOptions = {
        method: 'POST',
        headers: {
            'Accept': 'application/json',
            'Authorization': 'Key ' + PAT
        },
        body: raw
    };

    fetch('https://api.clarifai.com/v2/models', requestOptions)
        .then(response => response.text())
        .then(result => console.log(result))
        .catch(error => console.log('error', error));

</script>

//index.js file

////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, model ID, and model type ID.
// Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////////////

const USER_ID = 'YOUR_USER_ID_HERE';
// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = 'YOUR_PAT_HERE';
const APP_ID = 'YOUR_APP_ID_HERE';
// Change these to create your own model
const MODEL_ID = 'zero-shot-text-model';
const MODEL_TYPE_ID = 'zero-shot-text-classifier';

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require('clarifai-nodejs-grpc');

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set('authorization', 'Key ' + PAT);

stub.PostModels(
    {
        user_app_id: {
            'user_id': USER_ID,
            'app_id': APP_ID
        },
        models: [
            {
                id: MODEL_ID,
                model_type_id: MODEL_TYPE_ID,
                notes: 'zero shot text classifier'          
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error('Post models failed, status: ' + response.status.description);
        }
    }
);

package com.clarifai.example;

import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;

public class ClarifaiExample {

    ////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model type ID.
    // Change these strings to run your own example.
    ///////////////////////////////////////////////////////////////////////////////////////////

    static final String USER_ID = "YOUR_USER_ID_HERE";
    //Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String APP_ID = "YOUR_APP_ID_HERE";
    // Change these to create your own model    
    static final String MODEL_ID = "zero-shot-text-model";
    static final String MODEL_TYPE_ID = "zero-shot-text-classifier";
    
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////	

    public static void main(String[] args) {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
            .withCallCredentials(new ClarifaiCallCredentials(PAT));

        SingleModelResponse postModelsResponse = stub.postModels(
            PostModelsRequest.newBuilder()
            .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
            .addModels(
                Model.newBuilder()
                .setId(MODEL_ID)
                .setModelTypeId(MODEL_TYPE_ID) 
                .setNotes("zero shot text classifier")                
            ).build()
        );

        if (postModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post models failed, status: " + postModelsResponse.getStatus());
        }

    }

}

<?php

require __DIR__ . "/vendor/autoload.php";

/////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, model ID, and model type ID.
// Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////

$USER_ID = "YOUR_USER_ID_HERE";
// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
$APP_ID = "YOUR_APP_ID_HERE";
// Change these to create your own model
$MODEL_ID = "zero-shot-text-model";
$MODEL_TYPE_ID = "zero-shot-text-classifier";

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Model;
use Clarifai\Api\PostModelsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client->PostModels(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostModelsRequest([
            "user_app_id" => $userDataObject,
            "models" => [
                new Model([                    
                    "id" => $MODEL_ID,
                    "model_type_id" => $MODEL_TYPE_ID,
                    "notes" => "zero shot text classifier" 
                ]),
            ],
        ]),
        $metadata
    )->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " . $response->getStatus()->getDetails());
}

?>

curl -X POST "https://api.clarifai.com/v2/users/YOUR_USER_ID_HERE/apps/YOUR_APP_ID_HERE/models" \
  -H "Authorization: Key YOUR_PAT_HERE" \
  -H "Content-Type: application/json" \
  -d '{
    "model": {
      "id": "zero-shot-text-model",
      "model_type_id": "zero-shot-text-classifier",
      "notes": "zero shot text classifier"
    }
  }'

Next, import the model by creating a new version for it.

Python
JavaScript (REST)
Java
cURL

########################################################################################
# In this section, we set the user authentication, app ID, model ID, and concept IDs.
# Change these strings to run your own example.
########################################################################################

USER_ID = "YOUR_USER_ID_HERE"
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
APP_ID = "YOUR_APP_ID_HERE"
# Change these to import your own model
MODEL_ID = "zero-shot-text-model"
CONCEPT_ID_1 = "happy"
CONCEPT_ID_2 = "sad"

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
from google.protobuf.struct_pb2 import Struct

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

params = Struct()
params.update(
    {
        "toolkit":"HuggingFace",
        "pipeline_name":"zero-shot-classification",
        "model_name":"facebook/bart-large-mnli",
        "tokenizer_config":{},
        "use_gpu": True
    }
    )

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

post_model_versions = stub.PostModelVersions(
    service_pb2.PostModelVersionsRequest(
        user_app_id=userDataObject,
        model_id=MODEL_ID,
        model_versions=[
            resources_pb2.ModelVersion(
                import_info=resources_pb2.ImportInfo(
                    params=params,
                ),
                output_info=resources_pb2.OutputInfo(
                    data=resources_pb2.Data(
                        concepts=[
                            resources_pb2.Concept(id=CONCEPT_ID_1),
                            resources_pb2.Concept(id=CONCEPT_ID_2)
                            ]
                    ),
                ),
            )
        ],
    ),
    metadata=metadata,
)

if post_model_versions.status.code != status_code_pb2.SUCCESS:
    print(post_model_versions.status)
    raise Exception("Post models versions failed, status: " + post_model_versions.status.description)

<!--index.html file-->

<script>
    //////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and concept IDs.
    // Change these strings to run your own example.
    /////////////////////////////////////////////////////////////////////////////////////////
  
    const USER_ID = "YOUR_USER_ID_HERE";
    // Your PAT (Personal Access Token) can be found in the Account's Security section
    const PAT = "YOUR_PAT_HERE";
    const APP_ID = "YOUR_APP_ID_HERE";
    // Change these to import your own model
    const MODEL_ID = "zero-shot-text-model";
    const CONCEPT_ID_1 = "happy";
    const CONCEPT_ID_2 = "sad";
  
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////
  
    const raw = JSON.stringify({
        "user_app_id": {
            "user_id": USER_ID,
            "app_id": APP_ID
        },
        "model_versions": [{
            "import_info": {
                "params": {
                  "toolkit":"HuggingFace",
                  "pipeline_name":"zero-shot-classification",
                  "model_name":"facebook/bart-large-mnli",
                  "tokenizer_config":{},
                  "use_gpu": true
                }
            },
            "output_info": {
                "data": {
                    "concepts": [
                        {
                            "id": CONCEPT_ID_1
                        },
                        {
                            "id": CONCEPT_ID_2
                        }
                    ]
                }
            }
        }]
  
    });
  
    const requestOptions = {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Key " + PAT
        },
        body: raw
    };
  
    fetch(`https://api.clarifai.com/v2/models/${MODEL_ID}/versions`, requestOptions)
        .then(response => response.text())
        .then(result => console.log(result))
        .catch(error => console.log("error", error));
  
  </script>

package com.clarifai.example;

import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;
import com.google.protobuf.Struct;
import com.google.protobuf.Value;

public class ClarifaiExample {

    //////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and concept IDs.
    // Change these strings to run your own example.
    //////////////////////////////////////////////////////////////////////////////////////////

    static final String USER_ID = "YOUR_USER_ID_HERE";
    // Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String APP_ID = "YOUR_APP_ID_HERE";
    // Change these to import your own model
    static final String MODEL_ID = "zero-shot-text-model";
    static final String CONCEPT_ID_1 = "happy";
    static final String CONCEPT_ID_2 = "sad";

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////	

    public static void main(String[] args) {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        Struct.Builder params = Struct.newBuilder()
                .putFields("toolkit", Value.newBuilder().setStringValue("HuggingFace").build())
                .putFields("pipeline_name", Value.newBuilder().setStringValue("zero-shot-classification").build())
                .putFields("model_name", Value.newBuilder().setStringValue("facebook/bart-large-mnli").build())
                .putFields("tokenizer_config", Value.newBuilder().setStructValue(Struct.newBuilder()).build())
                .putFields("use_gpu", Value.newBuilder().setBoolValue(true).build());

        SingleModelResponse postModelVersionsResponse = stub.postModelVersions(
                PostModelVersionsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setModelId(MODEL_ID)
                        .addModelVersions(ModelVersion.newBuilder()
                                .setImportInfo(ImportInfo.newBuilder()
                                        .setParams(params)
                                )
                                .setOutputInfo(OutputInfo.newBuilder()
                                        .setData(Data.newBuilder()
                                                .addConcepts(Concept.newBuilder()
                                                        .setId(CONCEPT_ID_1)
                                                )
                                                .addConcepts(Concept.newBuilder()
                                                        .setId(CONCEPT_ID_2)
                                                )
                                        )
                                )
                        )
                        .build()
        );

        if (postModelVersionsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post model outputs failed, status: " + postModelVersionsResponse.getStatus());
        }

    }
}

curl -X POST "https://api.clarifai.com/v2/users/YOUR_USER_ID_HERE/apps/YOUR_APP_ID_HERE/models/zero-shot-text-model/versions" \
  -H "Authorization: Key YOUR_PAT_HERE" \
  -H "Content-Type: application/json" \
  -d '{
        "model_versions": [{
            "import_info": {
                "params": {
                  "toolkit":"HuggingFace",
                  "pipeline_name":"zero-shot-classification",
                  "model_name":"facebook/bart-large-mnli",
                  "tokenizer_config":{},
                  "use_gpu": true
                }
            },
            "output_info": {
                "data": {
                    "concepts": [
                        {
                            "id": "happy"
                        },
                        {
                            "id": "sad"
                        }
                    ]
                }
            }
        }] 
  }'

Text Generation

To import a text generation (text-to-text) model type, you need to start by creating it.

##########################################################################################
# In this section, we set the user authentication, app ID, model ID, and model type ID.
# Change these strings to run your own example.
#########################################################################################

USER_ID = 'YOUR_USER_ID_HERE'
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = 'YOUR_PAT_HERE'
APP_ID = 'YOUR_APP_ID_HERE'
# Change these to create your own model
MODEL_ID = 'text-generation-model'
MODEL_TYPE_ID = 'text-to-text'

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (('authorization', 'Key ' + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

post_models_response = stub.PostModels(
    service_pb2.PostModelsRequest(
        user_app_id=userDataObject,
        models=[
            resources_pb2.Model(
                id=MODEL_ID,
                model_type_id=MODEL_TYPE_ID,
                notes='text generation notes'         
            )
        ]
    ),
    metadata=metadata
)

if post_models_response.status.code != status_code_pb2.SUCCESS:
    print(post_models_response.status)
    raise Exception("Post models failed, status: " + post_models_response.status.description)

<!--index.html file-->

<script>
    ///////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model type ID.
    // Change these strings to run your own example.
    //////////////////////////////////////////////////////////////////////////////////////////

    const USER_ID = 'YOUR_USER_ID_HERE';
    // Your PAT (Personal Access Token) can be found in the Account's Security section
    const PAT = 'YOUR_PAT_HERE';
    const APP_ID = 'YOUR_APP_ID_HERE';
    // Change these to create your own model
    const MODEL_ID = 'text-generation-model';
    const MODEL_TYPE_ID = 'text-to-text';
    
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////

    const raw = JSON.stringify({
        'user_app_id': {
            'user_id': USER_ID,
            'app_id': APP_ID
        },
        'model': {
            'id': MODEL_ID,
            'model_type_id': MODEL_TYPE_ID,
            'notes': 'text generation notes'       
        }
    });

    const requestOptions = {
        method: 'POST',
        headers: {
            'Accept': 'application/json',
            'Authorization': 'Key ' + PAT
        },
        body: raw
    };

    fetch('https://api.clarifai.com/v2/models', requestOptions)
        .then(response => response.text())
        .then(result => console.log(result))
        .catch(error => console.log('error', error));

</script>

//index.js file

////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, model ID, and model type ID.
// Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////////////

const USER_ID = 'YOUR_USER_ID_HERE';
// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = 'YOUR_PAT_HERE';
const APP_ID = 'YOUR_APP_ID_HERE';
// Change these to create your own model
const MODEL_ID = 'text-generation-model';
const MODEL_TYPE_ID = 'text-to-text';

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require('clarifai-nodejs-grpc');

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set('authorization', 'Key ' + PAT);

stub.PostModels(
    {
        user_app_id: {
            'user_id': USER_ID,
            'app_id': APP_ID
        },
        models: [
            {
                id: MODEL_ID,
                model_type_id: MODEL_TYPE_ID,
                notes: 'text generation notes'          
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error('Post models failed, status: ' + response.status.description);
        }
    }
);

package com.clarifai.example;

import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;

public class ClarifaiExample {

    ////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model type ID.
    // Change these strings to run your own example.
    ///////////////////////////////////////////////////////////////////////////////////////////

    static final String USER_ID = "YOUR_USER_ID_HERE";
    //Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String APP_ID = "YOUR_APP_ID_HERE";
    // Change these to create your own model    
    static final String MODEL_ID = "text-generation-model";
    static final String MODEL_TYPE_ID = "text-to-text";
    
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////	

    public static void main(String[] args) {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
            .withCallCredentials(new ClarifaiCallCredentials(PAT));

        SingleModelResponse postModelsResponse = stub.postModels(
            PostModelsRequest.newBuilder()
            .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
            .addModels(
                Model.newBuilder()
                .setId(MODEL_ID)
                .setModelTypeId(MODEL_TYPE_ID) 
                .setNotes("text generation notes")                
            ).build()
        );

        if (postModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post models failed, status: " + postModelsResponse.getStatus());
        }

    }

}

<?php

require __DIR__ . "/vendor/autoload.php";

/////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, model ID, and model type ID.
// Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////

$USER_ID = "YOUR_USER_ID_HERE";
// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
$APP_ID = "YOUR_APP_ID_HERE";
// Change these to create your own model
$MODEL_ID = "text-generation-model";
$MODEL_TYPE_ID = "text-to-text";

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Model;
use Clarifai\Api\PostModelsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client->PostModels(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostModelsRequest([
            "user_app_id" => $userDataObject,
            "models" => [
                new Model([                    
                    "id" => $MODEL_ID,
                    "model_type_id" => $MODEL_TYPE_ID,
                    "notes" => "text generation notes" 
                ]),
            ],
        ]),
        $metadata
    )->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " . $response->getStatus()->getDetails());
}

?>

curl -X POST "https://api.clarifai.com/v2/users/YOUR_USER_ID_HERE/apps/YOUR_APP_ID_HERE/models" \
  -H "Authorization: Key YOUR_PAT_HERE" \
  -H "Content-Type: application/json" \
  -d '{
    "model": {
      "id": "text-generation-model",
      "model_type_id": "text-to-text",
      "notes": "text generation notes"
    }
  }'

Next, import the model by creating a new version for it.

Python
JavaScript (REST)
Java
cURL

########################################################################################
# In this section, we set the user authentication, app ID, and model ID.
# Change these strings to run your own example.
########################################################################################

USER_ID = "YOUR_USER_ID_HERE"
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
APP_ID = "YOUR_APP_ID_HERE"
# Change these to import your own model
MODEL_ID = "text-generation-model"

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
from google.protobuf.struct_pb2 import Struct

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

params = Struct()
params.update(
    {
        "toolkit":"HuggingFace",
        "pipeline_name":"text-generation",
        "model_name":"hf-internal-testing/tiny-xlm-roberta"
    }
    )

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

post_model_versions = stub.PostModelVersions(
    service_pb2.PostModelVersionsRequest(
        user_app_id=userDataObject,
        model_id=MODEL_ID,
        model_versions=[
            resources_pb2.ModelVersion(
                import_info=resources_pb2.ImportInfo(
                    params=params,
                )  
            )
        ],
    ),
    metadata=metadata,
)

if post_model_versions.status.code != status_code_pb2.SUCCESS:
    print(post_model_versions.status)
    raise Exception("Post models versions failed, status: " + post_model_versions.status.description)

<!--index.html file-->

<script>
    //////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, and model ID.
    // Change these strings to run your own example.
    /////////////////////////////////////////////////////////////////////////////////////////
  
    const USER_ID = "YOUR_USER_ID_HERE";
    // Your PAT (Personal Access Token) can be found in the Account's Security section
    const PAT = "YOUR_PAT_HERE";
    const APP_ID = "YOUR_APP_ID_HERE";
    // Change these to import your own model
    const MODEL_ID = "text-generation-model";   
  
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////
  
    const raw = JSON.stringify({
        "user_app_id": {
            "user_id": USER_ID,
            "app_id": APP_ID
        },
        "model_versions": [{
            "import_info": {
                "params": {
                  "toolkit":"HuggingFace",
                  "pipeline_name":"text-generation",
                  "model_name":"hf-internal-testing/tiny-xlm-roberta"
                }
            }
        }]
  
    });
  
    const requestOptions = {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Key " + PAT
        },
        body: raw
    };
  
    fetch(`https://api.clarifai.com/v2/models/${MODEL_ID}/versions`, requestOptions)
        .then(response => response.text())
        .then(result => console.log(result))
        .catch(error => console.log("error", error));
  
  </script>

package com.clarifai.example;

import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;
import com.google.protobuf.Struct;
import com.google.protobuf.Value;

public class ClarifaiExample {

    //////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, and model ID.
    // Change these strings to run your own example.
    //////////////////////////////////////////////////////////////////////////////////////////

    static final String USER_ID = "YOUR_USER_ID_HERE";
    // Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String APP_ID = "YOUR_APP_ID_HERE";
    // Change these to import your own model
    static final String MODEL_ID = "text-generation-model"; 
  
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////	

    public static void main(String[] args) {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        Struct.Builder params = Struct.newBuilder()
                .putFields("toolkit", Value.newBuilder().setStringValue("HuggingFace").build())
                .putFields("pipeline_name", Value.newBuilder().setStringValue("text-generation").build())
                .putFields("model_name", Value.newBuilder().setStringValue("hf-internal-testing/tiny-xlm-roberta").build());

        SingleModelResponse postModelVersionsResponse = stub.postModelVersions(
                PostModelVersionsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setModelId(MODEL_ID)
                        .addModelVersions(ModelVersion.newBuilder()
                                .setImportInfo(ImportInfo.newBuilder()
                                        .setParams(params)
                                )
                        )
                        .build()
        );

        if (postModelVersionsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post model outputs failed, status: " + postModelVersionsResponse.getStatus());
        }

    }
}

curl -X POST "https://api.clarifai.com/v2/users/YOUR_USER_ID_HERE/apps/YOUR_APP_ID_HERE/models/text-generation-model/versions" \
  -H "Authorization: Key YOUR_PAT_HERE" \
  -H "Content-Type: application/json" \
  -d '{
        "model_versions": [{
            "import_info": {
                "params": {
                  "toolkit":"HuggingFace",
                  "pipeline_name":"text-generation",
                  "model_name":"hf-internal-testing/tiny-xlm-roberta"
                }
            }
        }] 
  }'

Local Model Upload

Let’s illustrate how you could import a custom-built model from your local development environment directly into the Clarifai platform.

tip

Click here to learn how to build a model that meets Clarifai's requirements.

Text-Embedder

To import a locally-built text-embedder model type, you need to start by creating it.

##########################################################################################
# In this section, we set the user authentication, app ID, model ID, and model type ID.
# Change these strings to run your own example.
#########################################################################################

USER_ID = 'YOUR_USER_ID_HERE'
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = 'YOUR_PAT_HERE'
APP_ID = 'YOUR_APP_ID_HERE'
# Change these to create your own model
MODEL_ID = 'embedding-model'
MODEL_TYPE_ID = 'text-embedder'

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

metadata = (('authorization', 'Key ' + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

post_models_response = stub.PostModels(
    service_pb2.PostModelsRequest(
        user_app_id=userDataObject,
        models=[
            resources_pb2.Model(
                id=MODEL_ID,
                model_type_id=MODEL_TYPE_ID,
                notes='text embedding notes'         
            )
        ]
    ),
    metadata=metadata
)

if post_models_response.status.code != status_code_pb2.SUCCESS:
    print(post_models_response.status)
    raise Exception("Post models failed, status: " + post_models_response.status.description)

<!--index.html file-->

<script>
    ///////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model type ID.
    // Change these strings to run your own example.
    //////////////////////////////////////////////////////////////////////////////////////////

    const USER_ID = 'YOUR_USER_ID_HERE';
    // Your PAT (Personal Access Token) can be found in the Account's Security section
    const PAT = 'YOUR_PAT_HERE';
    const APP_ID = 'YOUR_APP_ID_HERE';
    // Change these to create your own model
    const MODEL_ID = 'embedding-model';
    const MODEL_TYPE_ID = 'text-embedder';
    
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////

    const raw = JSON.stringify({
        'user_app_id': {
            'user_id': USER_ID,
            'app_id': APP_ID
        },
        'model': {
            'id': MODEL_ID,
            'model_type_id': MODEL_TYPE_ID,
            'notes': 'text embedding notes'       
        }
    });

    const requestOptions = {
        method: 'POST',
        headers: {
            'Accept': 'application/json',
            'Authorization': 'Key ' + PAT
        },
        body: raw
    };

    fetch('https://api.clarifai.com/v2/models', requestOptions)
        .then(response => response.text())
        .then(result => console.log(result))
        .catch(error => console.log('error', error));

</script>

//index.js file

////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, model ID, and model type ID.
// Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////////////

const USER_ID = 'YOUR_USER_ID_HERE';
// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = 'YOUR_PAT_HERE';
const APP_ID = 'YOUR_APP_ID_HERE';
// Change these to create your own model
const MODEL_ID = 'embedding-model';
const MODEL_TYPE_ID = 'text-embedder';

/////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
/////////////////////////////////////////////////////////////////////////////

const { ClarifaiStub, grpc } = require('clarifai-nodejs-grpc');

const stub = ClarifaiStub.grpc();

// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set('authorization', 'Key ' + PAT);

stub.PostModels(
    {
        user_app_id: {
            'user_id': USER_ID,
            'app_id': APP_ID
        },
        models: [
            {
                id: MODEL_ID,
                model_type_id: MODEL_TYPE_ID,
                notes: 'text embedding notes'          
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error('Post models failed, status: ' + response.status.description);
        }
    }
);

package com.clarifai.example;

import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;

public class ClarifaiExample {

    ////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model type ID.
    // Change these strings to run your own example.
    ///////////////////////////////////////////////////////////////////////////////////////////

    static final String USER_ID = "YOUR_USER_ID_HERE";
    //Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String APP_ID = "YOUR_APP_ID_HERE";
    // Change these to create your own model    
    static final String MODEL_ID = "embedding-model";
    static final String MODEL_TYPE_ID = "text-embedder";
    
    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////	

    public static void main(String[] args) {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
            .withCallCredentials(new ClarifaiCallCredentials(PAT));

        SingleModelResponse postModelsResponse = stub.postModels(
            PostModelsRequest.newBuilder()
            .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
            .addModels(
                Model.newBuilder()
                .setId(MODEL_ID)
                .setModelTypeId(MODEL_TYPE_ID) 
                .setNotes("text embedding notes")                
            ).build()
        );

        if (postModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post models failed, status: " + postModelsResponse.getStatus());
        }

    }

}

<?php

require __DIR__ . "/vendor/autoload.php";

/////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, app ID, model ID, and model type ID.
// Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////

$USER_ID = "YOUR_USER_ID_HERE";
// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = "YOUR_PAT_HERE";
$APP_ID = "YOUR_APP_ID_HERE";
// Change these to create your own model
$MODEL_ID = "embedding-model";
$MODEL_TYPE_ID = "text-embedder";

///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////

use Clarifai\ClarifaiClient;
use Clarifai\Api\Model;
use Clarifai\Api\PostModelsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;

$client = ClarifaiClient::grpc();

$metadata = ["Authorization" => ["Key " . $PAT]];

$userDataObject = new UserAppIDSet([
    "user_id" => $USER_ID,
    "app_id" => $APP_ID,
]);

// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client->PostModels(
        // The request object carries the request along with the request status and other metadata related to the request itself
        new PostModelsRequest([
            "user_app_id" => $userDataObject,
            "models" => [
                new Model([                    
                    "id" => $MODEL_ID,
                    "model_type_id" => $MODEL_TYPE_ID,
                    "notes" => "text embedding notes" 
                ]),
            ],
        ]),
        $metadata
    )->wait();

// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
    throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
    throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " . $response->getStatus()->getDetails());
}

?>

curl -X POST "https://api.clarifai.com/v2/users/YOUR_USER_ID_HERE/apps/YOUR_APP_ID_HERE/models" \
  -H "Authorization: Key YOUR_PAT_HERE" \
  -H "Content-Type: application/json" \
  -d '{
    "model": {
      "id": "embedding-model",
      "model_type_id": "text-embedder",
      "notes": "text embedding notes"
    }
  }'

Next, import the model by creating a new version for it.

Python
JavaScript (REST)
Java
cURL

#############################################################################################
# In this section, we set the user authentication, app ID, model ID, and model ZIP URL.
# Change these strings to run your own example.
#############################################################################################

USER_ID = "YOUR_USER_ID_HERE"
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = "YOUR_PAT_HERE"
APP_ID = "YOUR_APP_ID_HERE"
# Change these to import your own model
MODEL_ID = "embedding-model"
MODEL_ZIP_URL = "s3://clarifai-testing-persistent-data/test_triton_upload/jina-embeddings-v2-base-en.zip"

##########################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
##########################################################################

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
from google.protobuf.struct_pb2 import Struct

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)

input__fields_params = Struct()
input__fields_params.update({"text": "text"})

output__fields_params = Struct()
output__fields_params.update({"embeddings": "embeddings"})

pretrained_config = resources_pb2.PretrainedModelConfig(
    input_fields_map=input__fields_params,
    output_fields_map=output__fields_params,
    model_zip_url=MODEL_ZIP_URL,
)

metadata = (("authorization", "Key " + PAT),)

userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)

post_model_versions = stub.PostModelVersions(
    service_pb2.PostModelVersionsRequest(
        user_app_id=userDataObject,
        model_id=MODEL_ID,
        model_versions=[
            resources_pb2.ModelVersion(pretrained_model_config=pretrained_config)
        ],
    ),
    metadata=metadata,
)

if post_model_versions.status.code != status_code_pb2.SUCCESS:
    print(post_model_versions.status)
    raise Exception("Post models versions failed, status: " + post_model_versions.status.description)

<!--index.html file-->

<script>
    //////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model ZIP URL.
    // Change these strings to run your own example.
    /////////////////////////////////////////////////////////////////////////////////////////////

    const USER_ID = "YOUR_USER_ID_HERE";
    // Your PAT (Personal Access Token) can be found in the Account's Security section
    const PAT = "YOUR_PAT_HERE";
    const APP_ID = "YOUR_APP_ID_HERE";
    // Change these to import your own model
    MODEL_ID = "embedding-model";
    MODEL_ZIP_URL = "s3://clarifai-testing-persistent-data/test_triton_upload/jina-embeddings-v2-base-en.zip";

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////

    const raw = JSON.stringify({
        "user_app_id": {
            "user_id": USER_ID,
            "app_id": APP_ID
        },
        "model_versions": [{
            "pretrained_model_config": {
                "model_zip_url": "s3://clarifai-testing-persistent-data/test_triton_upload/jina-embeddings-v2-base-en.zip",
                "input_fields_map": {
                    "text": "text"
                },
                "output_fields_map": {
                    "embeddings": "embeddings"
                }
            }
        }]
    });

    const requestOptions = {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Key " + PAT
        },
        body: raw
    };

    fetch(`https://api.clarifai.com/v2/models/${MODEL_ID}/versions`, requestOptions)
        .then(response => response.text())
        .then(result => console.log(result))
        .catch(error => console.log("error", error));

</script>

package com.clarifai.example;

import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;
import com.google.protobuf.Struct;
import com.google.protobuf.Value;

public class ClarifaiExample {

    ////////////////////////////////////////////////////////////////////////////////////////////
    // In this section, we set the user authentication, app ID, model ID, and model ZIP URL.
    // Change these strings to run your own example.
    ////////////////////////////////////////////////////////////////////////////////////////////

    static final String USER_ID = "YOUR_USER_ID_HERE";
    // Your PAT (Personal Access Token) can be found in the portal under Authentication
    static final String PAT = "YOUR_PAT_HERE";
    static final String APP_ID = "YOUR_APP_ID_HERE";
    // Change these to import your own model    
    static final String MODEL_ID = "embedding-model";
    static final String MODEL_ZIP_URL = "s3://clarifai-testing-persistent-data/test_triton_upload/jina-embeddings-v2-base-en.zip";

    ///////////////////////////////////////////////////////////////////////////////////
    // YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
    ///////////////////////////////////////////////////////////////////////////////////	
    
    public static void main(String[] args) {

        V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
                .withCallCredentials(new ClarifaiCallCredentials(PAT));

        Struct.Builder input__fields_params = Struct.newBuilder()
                .putFields("text", Value.newBuilder().setStringValue("text").build());

        Struct.Builder output__fields_params = Struct.newBuilder()
                .putFields("embeddings", Value.newBuilder().setStringValue("embeddings").build());

        SingleModelResponse postModelVersionsResponse = stub.postModelVersions(
                PostModelVersionsRequest.newBuilder()
                        .setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
                        .setModelId(MODEL_ID)
                        .addModelVersions(ModelVersion.newBuilder()
                                .setPretrainedModelConfig(PretrainedModelConfig.newBuilder()
                                        .setInputFieldsMap(input__fields_params)
                                        .setOutputFieldsMap(output__fields_params)
                                        .setModelZipUrl(MODEL_ZIP_URL)
                                )
                        )
                        .build()
        );

        if (postModelVersionsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
            throw new RuntimeException("Post model outputs failed, status: " + postModelVersionsResponse.getStatus());
        }

    }
}

curl -X POST "https://api.clarifai.com/v2/users/YOUR_USER_ID_HERE/apps/YOUR_APP_ID_HERE/models/embedding-model/versions" \
  -H "Authorization: Key YOUR_PAT_HERE" \
  -H "Content-Type: application/json" \
  -d '{
       "model_versions": [{
            "pretrained_model_config": {
                "model_zip_url": "s3://clarifai-testing-persistent-data/test_triton_upload/jina-embeddings-v2-base-en.zip",
                "input_fields_map": {
                    "text": "text"
                },
                "output_fields_map": {
                    "embeddings": "embeddings"
                }
            }            
        }]
     }'

Import Models

Hugging Face Importation​

Zero-Shot-Text-Classifier​

Text Generation​

Local Model Upload​

Text-Embedder​

What did you think of this doc?

Hugging Face Importation

Zero-Shot-Text-Classifier

Text Generation

Local Model Upload

Text-Embedder