Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.tinfoil.sh/llms.txt

Use this file to discover all available pages before exploring further.

​
Image Upload

Multimodal Models Only: Image processing requires models with vision capabilities. Currently, Qwen3-VL 30B, Kimi K2.5, Kimi K2.6, and Gemma 4 31B support image inputs. Other models (Llama, GPT-OSS) are text-only and cannot process images.
See the vision models and chat models pages for complete model specifications and multimodal capabilities.

​
How It Works

Image processing works through the chat/completions endpoint using base64-encoded images. Images are sent as data URLs in the message content alongside your text prompt.

​
Converting Images to Base64

There are several ways to convert your images to base64 format: 
# Convert image to base64
base64 -i image.jpg -o image_base64.txt

# Or use it directly in your terminal
base64 image.jpg | pbcopy  # Copies to clipboard on macOS

​
API Usage

from tinfoil import TinfoilAI
import base64
import mimetypes
from pathlib import Path

# Initialize client with Qwen3-VL vision model
client = TinfoilAI(
    api_key="<YOUR_API_KEY>",
)

# Read and encode image with proper MIME type detection
image_path = "image.jpg"
with open(image_path, "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

# Determine MIME type
mime_type, _ = mimetypes.guess_type(image_path)
if not mime_type or not mime_type.startswith('image/'):
    # Fallback based on file extension
    ext = Path(image_path).suffix.lower()
    mime_type_map = {
        '.jpg': 'image/jpeg',
        '.jpeg': 'image/jpeg',
        '.png': 'image/png',
        '.gif': 'image/gif',
        '.webp': 'image/webp'
    }
    mime_type = mime_type_map.get(ext, 'image/jpeg')

# Create completion with multimodal content
response = client.chat.completions.create(
    model="qwen3-vl-30b",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:{mime_type};base64,{base64_image}"
                }
            }
        ]
    }]
)

print(response.choices[0].message.content)

​
Best Practices

  1. Image Size: For optimal performance, resize large images before processing (recommended max: 4096x4096)
  2. Base64 Encoding: Ensure proper base64 encoding and include the correct MIME type in the data URL
  3. Multiple Images: You can include multiple images in a single chat completion by adding multiple image_url objects to the content array
  4. Compression: Consider compressing large images to reduce payload size and improve response times