daanelson/flan-t5-large:ebb257f7 – Run with an API on Replicate

Version

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daanelson /flan-t5-large:ebb257f7

Playground API

Input

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

npx create-replicate --model=daanelson/flan-t5-large

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 daanelson/flan-t5-large using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "daanelson/flan-t5-large:ebb257f78238566b1635040ca72f3c0907f97da5d9d2895419e7ffb804b84929",
  {
    input: {
      debug: false,
      top_p: 1,
      max_length: 50,
      temperature: 0.75,
      repetition_penalty: 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 daanelson/flan-t5-large using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "daanelson/flan-t5-large:ebb257f78238566b1635040ca72f3c0907f97da5d9d2895419e7ffb804b84929",
    input={
        "debug": False,
        "top_p": 1,
        "max_length": 50,
        "temperature": 0.75,
        "repetition_penalty": 1
    }
)

# The daanelson/flan-t5-large 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/daanelson/flan-t5-large/api#output-schema
    print(item, end="")

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 daanelson/flan-t5-large 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": "ebb257f78238566b1635040ca72f3c0907f97da5d9d2895419e7ffb804b84929",
    "input": {
      "debug": false,
      "top_p": 1,
      "max_length": 50,
      "temperature": 0.75,
      "repetition_penalty": 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/daanelson/flan-t5-large@sha256:ebb257f78238566b1635040ca72f3c0907f97da5d9d2895419e7ffb804b84929 \
  -i 'debug=false' \
  -i 'top_p=1' \
  -i 'max_length=50' \
  -i 'temperature=0.75' \
  -i 'repetition_penalty=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/daanelson/flan-t5-large@sha256:ebb257f78238566b1635040ca72f3c0907f97da5d9d2895419e7ffb804b84929
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "debug": false,
      "top_p": 1,
      "max_length": 50,
      "temperature": 0.75,
      "repetition_penalty": 1
    }
  }' \
  http://localhost:5000/predictions

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

Output

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