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Mantis LLava 7B v1.1 Vision

fal-ai/mantis-llava-7b-v11
A multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where interleaved text and images can be used fed as the input to generate responses.
Inference
Commercial use
PlaygroundAPI

About

Predict

1. Calling the API#

Install the client#

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

npm install --save @fal-ai/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 { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/mantis-llava-7b-v11", {
  input: {
    prompt: "What are the differences between these two images?",
    images: [{
      image_url: "https://llava-vl.github.io/static/images/monalisa.jpg"
    }, {
      image_url: "https://llava-vl.github.io/static/images/view.jpg"
    }]
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

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 { fal } from "@fal-ai/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 { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/mantis-llava-7b-v11", {
  input: {
    prompt: "What are the differences between these two images?",
    images: [{
      image_url: "https://llava-vl.github.io/static/images/monalisa.jpg"
    }, {
      image_url: "https://llava-vl.github.io/static/images/view.jpg"
    }]
  },
  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 { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/mantis-llava-7b-v11", {
  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 { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/mantis-llava-7b-v11", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

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 { fal } from "@fal-ai/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#

prompt string* required

Prompt to be used for the image description

images list<ImagePrompt>* required

List of images to be processed

{
  "prompt": "What are the differences between these two images?",
  "images": [
    {
      "image_url": "https://llava-vl.github.io/static/images/monalisa.jpg"
    },
    {
      "image_url": "https://llava-vl.github.io/static/images/view.jpg"
    }
  ]
}

Output#

output string* required

Response from the model

{
  "output": "The first image is a painting, which means it is an artistic representation of a scene or person, whereas the second image is a photograph, which is a direct capture of a real-life scene. The painting features a woman with a smile, but the second image does not have any people in it. The second image shows a dock extending into a lake, with trees in the background and mountains in the distance. This suggests that the second image is a landscape photograph, which is a type of photography that captures natural scenery."
}

Other types#

ImagePrompt#

image_url string* required

Image URL to be used for the image description