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LLaVA v1.5 13B

fal-ai/llavav15-13b
Inference
Commercial use

About

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1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/serverless-client

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import * as fal from "@fal-ai/serverless-client";

const result = await fal.subscribe("fal-ai/llavav15-13b", {
  input: {
    image_url: "https://llava-vl.github.io/static/images/monalisa.jpg",
    prompt: "Do you know who drew this painting?"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import * as fal from "@fal-ai/serverless-client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

3. Queue#

Submit a request#

The client API provides a convenient way to submit requests to the model.

import * as fal from "@fal-ai/serverless-client";

const { request_id } = await fal.queue.submit("fal-ai/llavav15-13b", {
  input: {
    image_url: "https://llava-vl.github.io/static/images/monalisa.jpg",
    prompt: "Do you know who drew this painting?"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import * as fal from "@fal-ai/serverless-client";

const status = await fal.queue.status("fal-ai/llavav15-13b", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import * as fal from "@fal-ai/serverless-client";

const result = await fal.queue.result("fal-ai/llavav15-13b", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import * as fal from "@fal-ai/serverless-client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Read more about file handling in our file upload guide.

5. Schema#

Input#

image_url string* required

URL of the image to be processed

prompt string* required

Prompt to be used for the image

max_tokens integer

Maximum number of tokens to generate Default value: 64

temperature float

Temperature for sampling Default value: 0.2

top_p float

Top P for sampling Default value: 1

{
  "image_url": "https://llava-vl.github.io/static/images/monalisa.jpg",
  "prompt": "Do you know who drew this painting?",
  "max_tokens": 64,
  "temperature": 0.2,
  "top_p": 1
}

Output#

output string* required

Generated output

partial boolean

Whether the output is partial

{
  "output": "Leonardo da Vinci"
}

Other types#