> ## Documentation Index
> Fetch the complete documentation index at: https://docs.together.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Llama 4 quickstart
> How to get the most out of the new Llama 4 models.
Together AI offers day 1 support for the new Llama 4 multilingual vision models that can analyze multiple images and respond to queries about them.
Register for a [Together AI account](https://api.together.ai/) to get an API key. New accounts come with free credits to start. Install the Together AI library for your preferred language.
## How to use Llama 4 Models
```python Python theme={null}
from together import Together
client = Together() # API key via api_key param or TOGETHER_API_KEY env var
# Query image with Llama 4 Maverick model
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What can you see in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
},
},
],
}
],
)
print(response.choices[0].message.content)
```
```typescript TypeScript theme={null}
import Together from "together-ai";
const together = new Together(); // API key via apiKey param or TOGETHER_API_KEY env var
async function main() {
const response = await together.chat.completions.create({
model: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
messages: [{
role: "user",
content: [
{ type: "text", text: "What can you see in this image?" },
{ type: "image_url", image_url: { url: "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png" }}
]
}]
});
console.log(response.choices[0].message.content);
}
main();
```
### Output
```
The image depicts a serene landscape of Yosemite National Park, featuring a river flowing through a valley surrounded by towering cliffs and lush greenery.
* **River:**
* The river is calm and peaceful, with clear water that reflects the surrounding scenery.
* It flows gently from the bottom-left corner to the center-right of the image.
* The riverbank is lined with rocks and grasses, adding to the natural beauty of the scene.
* **Cliffs:**
* The cliffs are massive and imposing, rising steeply from the valley floor.
* They are composed of light-colored rock, possibly granite, and feature vertical striations.
* The cliffs are covered in trees and shrubs, which adds to their rugged charm.
* **Trees and Vegetation:**
* The valley is densely forested, with tall trees growing along the riverbanks and on the cliffsides.
* The trees are a mix of evergreen and deciduous species, with some displaying vibrant green foliage.
* Grasses and shrubs grow in the foreground, adding texture and color to the scene.
* **Sky:**
* The sky is a brilliant blue, with only a few white clouds scattered across it.
* The sun appears to be shining from the right side of the image, casting a warm glow over the scene.
In summary, the image presents a breathtaking view of Yosemite National Park, showcasing the natural beauty of the valley and its surroundings. The calm river, towering cliffs, and lush vegetation all contribute to a sense of serenity and wonder.
```
### Llama4 Notebook
If you'd like to see common use-cases in code see our [notebook here](https://github.com/togethercomputer/together-cookbook/blob/main/Getting_started_with_Llama4.ipynb).
## Llama 4 Model Details
### Llama 4 Maverick
* **Model String**: *meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8*
* **Specs**:
* 17B active parameters (400B total)
* 128-expert MoE architecture
* 524,288 context length (will be increased to 1M)
* Support for 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese
* Multimodal capabilities (text + images)
* Support Function Calling
* **Best for**: Enterprise applications, multilingual support, advanced document intelligence
* **Knowledge Cutoff**: August 2024
### Llama 4 Scout (Deprecated)
Llama 4 Scout (`meta-llama/Llama-4-Scout-17B-16E-Instruct`) has been deprecated. Use Llama 4 Maverick (`meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`) instead.
## Function Calling
```python Python theme={null}
import os
import json
import openai
client = openai.OpenAI(
base_url="https://api.together.ai/v1",
api_key=os.environ["TOGETHER_API_KEY"],
)
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
},
},
},
},
}
]
messages = [
{
"role": "system",
"content": "You are a helpful assistant that can access external functions. The responses from these function calls will be appended to this dialogue. Please provide responses based on the information from these function calls.",
},
{
"role": "user",
"content": "What is the current temperature of New York, San Francisco and Chicago?",
},
]
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
messages=messages,
tools=tools,
tool_choice="auto",
)
print(
json.dumps(
response.choices[0].message.model_dump()["tool_calls"],
indent=2,
)
)
```
### Output
```json JSON theme={null}
[
{
"id": "call_1p75qwks0etzfy1g6noxvsgs",
"function": {
"arguments": "{\"location\":\"New York, NY\",\"unit\":\"fahrenheit\"}",
"name": "get_current_weather"
},
"type": "function"
},
{
"id": "call_aqjfgn65d0c280fjd3pbzpc6",
"function": {
"arguments": "{\"location\":\"San Francisco, CA\",\"unit\":\"fahrenheit\"}",
"name": "get_current_weather"
},
"type": "function"
},
{
"id": "call_rsg8muko8hymb4brkycu3dm5",
"function": {
"arguments": "{\"location\":\"Chicago, IL\",\"unit\":\"fahrenheit\"}",
"name": "get_current_weather"
},
"type": "function"
}
]
```
## Query models with multiple images
Currently this model supports **5 images** as input.
```python Python theme={null}
# Multi-modal message with multiple images
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Compare these two images."},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
},
},
{
"type": "image_url",
"image_url": {
"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/slack.png"
},
},
],
}
],
)
print(response.choices[0].message.content)
```
### Output
```
The first image is a collage of multiple identical landscape photos showing a natural scene with rocks, trees, and a stream under a blue sky. The second image is a screenshot of a mobile app interface, specifically the navigation menu of the Canva app, which includes icons for Home, DMs (Direct Messages), Activity, Later, Canvases, and More.
### Comparison:
1. **Content**:
- The first image focuses on a natural landscape.
- The second image shows a digital interface from an app.
2. **Purpose**:
- The first image could be used for showcasing nature, design elements in graphic work, or as a background.
- The second image represents the functionality and layout of the Canva app's navigation system.
3. **Visual Style**:
- The first image has vibrant colors and realistic textures typical of outdoor photography.
- The second image uses flat design icons with a simple color palette suited for user interface design.
4. **Context**:
- The first image is likely intended for artistic or environmental contexts.
- The second image is relevant to digital design and app usability discussions.
```
## Llama 4 Use-cases
### Llama 4 Maverick:
* **Instruction following and Long context ICL**: Very consistent in following precise instructions with in-context learning across very long contexts
* **Multilingual customer support**: Process support tickets with screenshots in 12 languages to quickly diagnose technical issues
* **Multimodal capabilities**: Particularly strong at OCR and chart/graph interpretation
* **Agent/tool calling work**: Designed for agentic workflows with consistent tool calling capabilities
### Llama 4 Scout (Deprecated):
Llama 4 Scout has been deprecated. Use Llama 4 Maverick instead for similar capabilities including summarization, function calling, long context RAG, and multilingual support.