Text Generation (Vision)

Vision models behave similarly to text models, but they can accept and interpret images as well.

Models available

Available text models:

• Qwen2.5-VL-7B-Instruct: An advanced vision-lanauge model designed to understand and process both visual and textual inputs.

Using the Models

1. Go to the Nebula Block website.

2. Log in, and ensure you have enough credits.

3. Click on the "Serverless Endpoints" tab and select your model.

4. Select your parameters, enter your text (and image, if applicable) and just press Enter!

The parameters to tweak are the same as in Text Generation, with the addition of the following:

• Image: The image to be processed by the model.

Through API Endpoint

This option is to use our API endpoint directly in your projects. Below are some code snippets to get you started!

NOTE: Don't forget to use your API key. See the API Reference and the Overview for more details on authentication.

Using cURL

curl -X POST "https://inference.nebulablock.com/v1/chat/completions" \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $NEBULA_API_KEY" \
    --data-raw '{
        "messages": [
			{"role":"user","content":[
			{"type":"image_url","image_url":
			{"url":"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}},
			{"type":"text","text":"What is this image?"}
		]}],
        "model": "Qwen/Qwen2.5-VL-7B-Instruct",
        "max_tokens": null, 
        "temperature": 1,
        "top_p": 0.9,
        "stream": false
    }'

Using Python

import requests 
import os
 
url = "https://inference.nebulablock.com/v1/chat/completions"

headers = { 
    "Content-Type": "application/json", 
    "Authorization": f"Bearer {os.environ.get('NEBULA_API_KEY')}" 
} 
 
data = {
    "messages":[
		{"role":"user","content":[
		{"type":"image_url","image_url":
		{"url":"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}},
		{"type":"text","text":"What is this image?"}
	]}],
    "model":"Qwen/Qwen2.5-VL-7B-Instruct",
    "max_tokens":None,
    "temperature":1,
    "top_p":0.9,
    "stream":False
}

response = requests.post(url, headers=headers, json=data) 
print(response.json())

Using JavaScript

const url = "https://inference.nebulablock.com/v1/chat/completions";

const headers = {
    "Content-Type": "application/json",
    "Authorization": `Bearer sk-kmlKFCXGBZl-PS0MLnpzAw`
};

const data = {
    messages: [
        {"role":"user","content":[
        {"type":"image_url","image_url":
        {"url":"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}},
        {"type":"text","text":"What is this image?"}
    ]}],
    model: 'Qwen/Qwen2.5-VL-7B-Instruct',
    max_tokens: null,
    temperature: 1,
    top_p: 0.9,
    stream: false
};

fetch(url, {
    method: 'POST',
    headers: headers,
    body: JSON.stringify(data)
})
    .then(response => response.json())
    .then(data => {
        console.log(JSON.stringify(data, null, 2));
    })
    .catch(error => console.error('Error:', error));

Selecting a Model

To specify the desired model, use this mapping for the model_name:

• Qwen2.5-VL-7B-Instruct: Qwen/Qwen2.5-VL-7B-Instruct

Response Example

A successful generation response (non-streaming) will contain a chat.completion object, and should look like this:

    {
    "id": "chatcmpl-7ba48f119a564f4ea02b6a41386a3e40",
    "created": 1740689977,
    "model": "Qwen/Qwen2.5-VL-7B-Instruct",
    "object": "chat.completion",
    "system_fingerprint": null,
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "This image shows ....",
                "role": "assistant",
                "tool_calls": null,
                "function_call": null
            }
        }
    ],
    "usage": {
        "completion_tokens": 91,
        "prompt_tokens": 3604,
        "total_tokens": 3695,
        "completion_tokens_details": null,
        "prompt_tokens_details": null
    },
    "service_tier": null,
    "prompt_logprobs": null
}

As is the case with text generation, this represents the entire generated response, and setting the stream parameter to True will return the response in chunks:

{
    "id": "chatcmpl-3812731562554b23a32dd80fbb7d0d09",
    "created": 1740692435,
    "model": "Qwen/Qwen2.5-VL-7B-Instruct",
    "object": "chat.completion.chunk",
    "choices": [
        {
            "index": 0,
            "delta": {
                "content": " setting"
            }
        }
    ]
}
{ 
  ...
}
...

with the collection of chunks forming the entire response.

Feel free to explore refer to the API Reference for more details.