> For the complete documentation index, see [llms.txt](https://docs.vaikerai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.vaikerai.com/guides/running-models-on-vaikerai-using-python.md).

# Running Models on VaikerAI Using Python

Learn how to integrate and run machine learning models on VaikerAI directly from your Python code, whether it's in an app, notebook, or script.

### 1. Install the Python Library

To interact with VaikerAI, you'll need to install our open-source Python client. Use pip to install it:

```bash
pip install vaikerai
```

### 2. Authenticate

Before running models, you'll need to authenticate with VaikerAI. Generate an API token by visiting [your account's API tokens page](https://vaikerai.com/account/api-tokens). Copy the token and set it as an environment variable in your shell:

```bash
export REPLICATE_API_TOKEN=8UY56_....
```

### 3. Run a Model

You can run any public model on VaikerAI with just a few lines of Python. Here’s an example that uses the `stability-ai/sdxl` model to generate an image based on a text prompt:

```python
import vaikerai

output = vaikerai.run(
  "stability-ai/sdxl:39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b",
  input={"prompt": "an iguana on the beach, pointillism"}
)
print(output)
```

The output will be a URL to the generated image:

```plaintext
['https://files.vaikerai.com/pbxt/VJyWBjIYgqqCCBEhpkCqdevTgAJbl4fg62aO4o9A0x85CgNSA/out-0.png']
```

### 4. Using Local Files as Inputs

Some models require files as input. You can use local files or provide a file's HTTPS URL.

#### Example: Using a Local File

Here’s an example using a local image file with the `LLaVA` vision model, which processes an image and a text prompt to generate a response:

```python
import vaikerai

image = open("my_fridge.jpg", "rb")
output = vaikerai.run(
    "yorickvp/llava-13b:a0fdc44e4f2e1f20f2bb4e27846899953ac8e66c5886c5878fa1d6b73ce009e5",
    input={
        "image": image,
        "prompt": "Here's what's in my fridge. What can I make for dinner tonight?"
    }
)
print(output)
```

The model's response might be:

```plaintext
You have a well-stocked refrigerator filled with various fruits, vegetables, and ...
```

### 5. Using URLs as Inputs

If your file is already hosted online or is large, using its URL as input is more efficient.

#### Example: Using a URL

Here’s an example using a public HTTPS URL of an image:

```python
image = "https://example.com/my_fridge.jpg"
output = vaikerai.run(
    "yorickvp/llava-13b:a0fdc44e4f2e1f20f2bb4e27846899953ac8e66c5886c5878fa1d6b73ce009e5",
    input={
        "image": image,
        "prompt": "Here's what's in my fridge. What can I make for dinner tonight?"
    }
)
print(output)
```

The model will return a text response similar to:

```plaintext
You have a well-stocked refrigerator filled with various fruits, vegetables, and ...
```

### 6. Handling Output

Some models stream their output as they process the input. These models return an iterator, allowing you to process each chunk of output as it becomes available.

#### Example: Streaming Output

Here’s how to handle streamed output from the `mistralai/mixtral-8x7b-instruct-v0.1` model:

```python
iterator = vaikerai.run(
  "mistralai/mixtral-8x7b-instruct-v0.1",
  input={"prompt": "Who was Dolly the sheep?"},
)
for text in iterator:
    print(text)
    
```

As the model runs, you might see output like this:

```plaintext
🐑
Dolly the sheep was the first mammal to be successfully cloned from an adult cell...
```

### Next Steps

For more detailed information and advanced usage, refer to the full Python client documentation available on [GitHub](https://github.com/vaikerai/python-client).

***