Running Different Models

Having installed Ollama and run your first LLaMA 3.2 model locally, it’s time to explore Ollama’s full model registry and try a vision-capable model. In this guide, we’ll:

- Browse and filter models on the Ollama website  
- Examine a LLaVA multimodal model’s specs  
- Run vision-enabled models locally via CLI and API  
- Compare other image-capable options  

---

## 1. Browse Ollama’s Model Registry

Head over to the [Ollama website](https://ollama.com) and click **Models** in the navigation bar. You’ll see a list of supported AI models complete with architecture, parameter count, and quantization settings:

![The image shows a webpage from Ollama, displaying a list of AI models such as "deepseek-r1" and "llama3.3" with details about their parameters and performance. There are options to filter models by categories like Embedding, Vision, and Tools.](/images/Running-Local-LLMs-With-Ollama-Running-Different-Models/ollama-ai-models-list-webpage.jpg)

Scroll down and select the **Vision** category. The top entry is a LLaMA-based vision model (LLaVA). Click it to view its details.

---

## 2. Inspect the Vision Model Specification

On the model detail page, you’ll find:

| Model   | Parameters | Quantization | Use Case               |
|---------|------------|--------------|------------------------|
| LLaVA   | 7.24 B     | Q4_0 (text)  | Image understanding    |
| CLIP    | 312 M      | F16          | Vision encoder support |

Below is the YAML metadata for LLaVA:

```yaml
model:
  arch: llama
  parameters: 7.24B
  quantization: Q4_0
projector:
  arch: clip
  parameters: 312M
  quantization: F16
params:
  stop: ["[INST]", "[/INST]"]
template: "[INST] {{ if .System }}{{ .System }} {{ end }}{{ .Prompt }}…"
license: "Apache License Version 2.0, January 2004"
```text
,[object Object],

---

## 3. Using CLI and Local API

### Interactive CLI

```bash
# Launch LLaVA in interactive mode
$ ollama run llava
>>> send a message (/1 for help)
>>> What's in this image? /Users/jmorgan/Desktop/smile.png
The image features a yellow smiley face, which is likely the central focus of the picture.
```text

### Local API Call

```bash
# Send a base64-encoded image via curl
$ curl http://localhost:11434/api/generate -d '{
  "model": "llava",
  "prompt": "What is in this picture?",
  "images": ["iVBORw0KGgoAAAANSUhEUgAAAGAAABmCAYAAABxVAAAAAElFTkSuQmCC"]
}'
```text

---

## 4. Run the Model Locally

If you haven’t pulled LLaVA yet, Ollama will download it when you first run:

![The image shows a webpage for "LLaVA," a multimodal model combining a vision encoder and Vicuna for visual and language understanding, updated to version 1.6. It includes details about the model's parameters, quantization, and license information.](/images/Running-Local-LLMs-With-Ollama-Running-Different-Models/llava-multimodal-model-v1-6.jpg)

```bash
$ ollama run llava
>>> send a message (/1 for help)
```text

Once the prompt appears, test it with your own image—here’s an example using a local `logo.jpeg` file:

```bash
$ ollama run llava
>>> what is there in this image? ./logo.jpeg
Added image './logo.jpeg'
The image features a logo with the text "KodeKloud" in lowercase letters. Above it, there’s an icon representing a cloud or hosting service. To the left of the text "KodeKloud," there’s a stylized cube symbol suggesting programming or technology.
```text

You can continue the conversation:

```bash
>>> what colors are there in the image?
The image features:
1. Blue for the cloud icon and part of the cube.
2. Black or dark gray for the "KodeKloud" text.
3. White/light gray for the background.
>>> /bye

5. Explore Other Image-Capable Models

Ollama’s registry includes smaller or specialized vision models—like a compact LLaVA fine-tuned from Phi 3 Mini:

Compare models by size, speed, and accuracy to find the best fit for your project.

Next Steps

• Try different models under the Vision category
• Experiment with batch API calls for automated workflows
• Review Ollama CLI Documentation for advanced usage

Happy local inference!