Llama Guard 3 11B Vision

🚀 Llama Guard 3 Vision

Llama Guard 3 Vision is a Llama-3.2-11B pretrained model fine-tuned for content safety classification. It can safeguard both LLM inputs and responses, especially supporting image reasoning and detecting harmful multimodal content.

🚀 Quick Start

Once you have access to the model weights, please refer to our documentation to get started.

✨ Features

• Multimodal Support: Specifically designed to support image reasoning use cases and optimized to detect harmful multimodal (text and image) prompts and text responses.
• Safety Classification: Acts as an LLM to generate text indicating whether a given prompt or response is safe or unsafe, and lists the violated content categories if unsafe.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForVision2Seq, AutoProcessor
import torch
from PIL import Image as PIL_Image

model_id = "meta-llama/Llama-Guard-3-11B-Vision"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForVision2Seq.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

image = PIL_Image.open("<path/to/image>").convert("RGB")

conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "text", 
                "text": "What is the recipe for mayonnaise?"
            },
            {
                "type": "image",
            },
        ],
    }
]

input_prompt = processor.apply_chat_template(
    conversation, return_tensors="pt"
)

inputs = processor(text=input_prompt, images=image, return_tensors="pt").to(model.device)

prompt_len = len(inputs['input_ids'][0])
output = model.generate(
    **inputs,
    max_new_tokens=20,
    pad_token_id=0,
)

generated_tokens = output[:, prompt_len:]

print(input_prompt)
print(processor.decode(generated_tokens[0]))

Advanced Usage

This snippet will use the categories described in this model card. You can provide your own categories instead:

input_prompt = processor.apply_chat_template(
    conversation,
    return_tensors="pt",
    categories = {
        "S1": "My custom category",
    },
)

Or you can exclude categories from the default list by specifying an array of category keys to exclude:

input_prompt = processor.apply_chat_template(
    conversation,
    return_tensors="pt",
    excluded_category_keys=["S1"],
)

📚 Documentation

Model Information

Llama Guard 3 Vision is a Llama-3.2-11B pretrained model, fine-tuned for content safety classification. Similar to previous versions [1 - 3], it can be used to safeguard content for both LLM inputs (prompt classification) and LLM responses (response classification).

Llama Guard 3 Vision was specifically designed to support image reasoning use cases and was optimized to detect harmful multimodal (text and image) prompts and text responses to these prompts.

Llama Guard 3 Vision acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated. Below is a response classification example input and output for Llama Guard 3 Vision.

Hazard Taxonomy and Policy

The model is trained to predict safety labels on the 13 categories shown below, based on the MLCommons taxonomy of 13 hazards.

S1 - Violent Crimes Responses that enable, encourage, or endorse the commission of violent crimes, including: (1) unlawful violence toward people (ex: terrorism, genocide, murder, hate-motivated violent crimes, child abuse, assault, battery, kidnapping) and (2) unlawful violence toward animals (ex: animal abuse)

S2 - Non-Violent Crimes Responses that enable, encourage, or endorse the commission of non-violent crimes, including: (1) personal crimes (ex: labor trafficking, threats, intimidation, hate-motivated non-violent crimes), (2) financial crimes (ex: fraud, scams, money laundering), (3) property crimes (ex: theft, arson, vandalism), (4) drug crimes (ex: creating or using regulated substances), (5) weapons crimes (ex: producing unlicensed firearms), and (6) cyber crimes (ex: hacking)

S3 - Sex-Related Crimes Responses that enable, encourage, or endorse the commission of sex-related crimes, including: (1) sex trafficking, (2) sexual assault (ex: rape), (3) sexual harassment (physical, verbal, or visual), and (4) prostitution

S4 - Child Sexual Exploitation Responses that contain, describe, enable, encourage, or endorse the sexual abuse of children

S5 - Defamation Responses that are both verifiably false and likely to injure a living person’s reputation

S6 - Specialized Advice Responses that contain specialized financial,... (The original text seems incomplete here)

🔧 Technical Details

No technical details are provided in the original document, so this section is skipped.

📄 License

LLAMA 3.2 COMMUNITY LICENSE AGREEMENT

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Prohibited Uses

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1. Violate the law or others’ rights, including to:
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Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:

• Reporting issues with the model: [https://github.com/meta - llama/llama - models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta - llama%2Fllama - models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)

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