Llama 4 Scout 17B 16E Unsloth

🚀 Llama 4 Model

The Llama 4 collection consists of natively multimodal AI models that offer text and multimodal experiences, leveraging a mixture - of - experts architecture for excellent performance in text and image understanding.

🚀 Quick Start

This version currently only works with Unsloth! See our collection for versions of Llama 4 including 4 - bit & 16 - bit formats. Unsloth's Dynamic Quants is selectively quantized, greatly improving accuracy over standard 4 - bit.

✨ Features

• Multimodal Capability: The Llama 4 models are natively multimodal, supporting both text and image understanding.
• Mixture - of - Experts Architecture: Utilizes this architecture to offer industry - leading performance in various tasks.
• Multiple Language Support: Supports Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.

📦 Installation

Please, make sure you have transformers v4.51.0 installed, or upgrade using pip install -U transformers.

💻 Usage Examples

Basic Usage

from transformers import AutoProcessor, Llama4ForConditionalGeneration
import torch

model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"

processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(
    model_id,
    attn_implementation="flex_attention",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

url1 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
url2 = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/cat_style_layout.png"
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": url1},
            {"type": "image", "url": url2},
            {"type": "text", "text": "Can you describe how these two images are similar, and how they differ?"},
        ]
    },
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
)

response = processor.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])[0]
print(response)
print(outputs[0])

📚 Documentation

Model Information

• Model developer: Meta
• Model Architecture: The Llama 4 models are auto - regressive language models that use a mixture - of - experts (MoE) architecture and incorporate early fusion for native multimodality.
• Supported languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.
• Model Release Date: April 5, 2025
• Status: This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback.
• License: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta - llama/llama - models/blob/main/models/llama4/LICENSE](https://github.com/meta - llama/llama - models/blob/main/models/llama4/LICENSE)
• Where to send questions or comments about the model: Instructions on how to provide feedback or comments on the model can be found in the Llama [README](https://github.com/meta - llama/llama - models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta - llama/llama - cookbook).

Intended Use

• Intended Use Cases: Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant - like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 Community License allows for these use cases.
• Out - of - scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card.

Hardware and Software

• Training Factors: We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine - tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
• Training Energy Use: Model pre - training utilized a cumulative of 7.38M GPU hours of computation on H100 - 80GB (TDP of 700W) type hardware. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
• Training Greenhouse Gas Emissions: Estimated total location - based greenhouse gas emissions were 1,999 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market - based greenhouse gas emissions for training were 0 tons CO2eq.

The methodology used to determine training energy use and greenhouse gas emissions can be found here. Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.

Training Data

• Overview: Llama 4 Scout was pretrained on ~40 trillion tokens and Llama 4 Maverick was pretrained on ~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI.
• Data Freshness: The pretraining data has a cutoff of August 2024.

Benchmarks

Pre - trained models

Instruction tuned models

License

The Llama 4 models are released under the Llama 4 Community License Agreement. The full text of the license is available at [https://github.com/meta - llama/llama - models/blob/main/models/llama4/LICENSE](https://github.com/meta - llama/llama - models/blob/main/models/llama4/LICENSE).