ml6/julius:0df0d4f8 – Run with an API on Replicate

Version

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ml6 /julius:0df0d4f8

Playground API

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=ml6/julius

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run ml6/julius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ml6/julius:0df0d4f867a4d0311ab6a7ca694d39685e8d650247a5053f2f21afc573696b81",
  {
    input: {
      top_k: 40,
      softmax_temperature: 1
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run ml6/julius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ml6/julius:0df0d4f867a4d0311ab6a7ca694d39685e8d650247a5053f2f21afc573696b81",
    input={
        "top_k": 40,
        "softmax_temperature": 1
    }
)

# The ml6/julius model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/ml6/julius/api#output-schema
    print(item)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run ml6/julius using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "0df0d4f867a4d0311ab6a7ca694d39685e8d650247a5053f2f21afc573696b81",
    "input": {
      "top_k": 40,
      "softmax_temperature": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/ml6/julius@sha256:0df0d4f867a4d0311ab6a7ca694d39685e8d650247a5053f2f21afc573696b81 \
  -i 'top_k=40' \
  -i 'softmax_temperature=1'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/ml6/julius@sha256:0df0d4f867a4d0311ab6a7ca694d39685e8d650247a5053f2f21afc573696b81
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "top_k": 40,
      "softmax_temperature": 1
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

No output yet! Press "Submit" to start a prediction.