Hugging Face's logo

meta-llama
/
LlamaGuard-7b

Text Generation Transformers Safetensors PyTorch English llama facebook meta llama-2 conversational Inference Endpoints text-generation-inference
Model card Files Community
24
Edit model card

You need to share contact information with Meta to access this model

The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.

LLAMA 2 COMMUNITY LICENSE AGREEMENT

"Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
"Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/.
"Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
"Llama 2" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
"Llama Materials" means, collectively, Meta's proprietary Llama 2 and documentation (and any portion thereof) made available under this Agreement.
"Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).

By clicking "I Accept" below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.

  1. License Rights and Redistribution.
    a. Grant of Rights. You are granted a non-exclusive, worldwide, non- transferable and royalty-free limited license under Meta's intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.
    b. Redistribution and Use.
    i. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party.
    ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.
    iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a "Notice" text file distributed as a part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved."
    iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement.
    v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof).

  2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.

  3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.

  4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.

  5. Intellectual Property.
    a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials.
    b. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
    c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.

  6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.

  7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.

Llama 2 Acceptable Use Policy

Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at ai.meta.com/llama/use-policy.

Prohibited Uses

We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:

  1. Violate the law or others’ rights, including to:
    1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
      1. Violence or terrorism
      2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
      3. Human trafficking, exploitation, and sexual violence
      4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
      5. Sexual solicitation
      6. Any other criminal activity
    2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
    3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
    4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
    5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
    6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
    7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
  2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
    1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
    2. Guns and illegal weapons (including weapon development)
    3. Illegal drugs and regulated/controlled substances
    4. Operation of critical infrastructure, transportation technologies, or heavy machinery
    5. Self-harm or harm to others, including suicide, cutting, and eating disorders
    6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
  3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
    1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
    2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
    3. Generating, promoting, or further distributing spam
    4. Impersonating another individual without consent, authorization, or legal right
    5. Representing that the use of Llama 2 or outputs are human-generated
    6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
    7. Fail to appropriately disclose to end users any known dangers of your AI system
      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:

Log in or Sign Up to review the conditions and access this model content.

Model Details

This repository contains the model weights both in the vanilla Llama format and the Hugging Face transformers format. If you have not received access, please review this discussion

Llama-Guard is a 7B parameter Llama 2-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories. Here is an example:

In order to produce classifier scores, we look at the probability for the first token, and turn that into an “unsafe” class probability. Model users can then make binary decisions by applying a desired threshold to the probability scores.

Training and Evaluation

Training Data

We use a mix of prompts that come from the Anthropic dataset and redteaming examples that we have collected in house, in a separate process from our production redteaming. In particular, we took the prompts only from the Anthropic dataset, and generated new responses from our in-house LLaMA models, using jailbreaking techniques to elicit violating responses. We then annotated Anthropic data (prompts & responses) in house, mapping labels according to the categories identified above. Overall we have ~13K training examples.

Taxonomy of harms and Risk Guidelines

As automated content risk mitigation relies on classifiers to make decisions about content in real time, a prerequisite to building these systems is to have the following components:

  • A taxonomy of risks that are of interest – these become the classes of a classifier.
  • A risk guideline that determines where we put the line between encouraged and discouraged outputs for each risk category in the taxonomy. Together with this model, we release an open taxonomy inspired by existing open taxonomies such as those employed by Google, Microsoft and OpenAI in the hope that it can be useful to the community. This taxonomy does not necessarily reflect Meta's own internal policies and is meant to demonstrate the value of our method to tune LLMs into classifiers that show high performance and high degrees of adaptability to different policies.

The Llama-Guard Safety Taxonomy & Risk Guidelines

Below, we provide both the harm types themselves under this taxonomy and also examples of the specific kinds of content that would be considered harmful under each category:

  • Violence & Hate encompasses statements that encourage or could help people plan or engage in violence. Similarly, statements that advocate discrimination, contain slurs, or voice hateful sentiments against people based on their sensitive personal characteristics (ex: race, color, religion, national origin, sexual orientation, gender, gender identity, or disability) would also be considered inappropriate under this category.
  • Sexual Content encompasses statements encouraging someone (who could be underage) to engage in specific sex acts. Similarly, sexually explicit (i.e., erotic) statements would also be considered inappropriate under this category.
  • Guns & Illegal Weapons encompasses statements that specifically encourage, condone, or could help people plan or execute specific crimes involving the illegal acquisition, creation, or use of guns or illegal weapons (ex: explosives, biological agents, or chemical weapons), including instructions on how to create or use them.
  • Regulated or Controlled Substances encompasses statements that specifically encourage or could help people to illegally produce, transfer, or consume regulated or controlled substances like illegal drugs, tobacco, alcohol, or cannabis.
  • Suicide & Self Harm encompasses statements that encourage, condone, or enable people to harm themselves (ex: by providing instructions or information on methods of self-harm). When people express or imply an intent to harm themselves, any response other than one that provides links to appropriate health resources would be considered inappropriate (even if entirely supportive).
  • Criminal Planning encompasses miscellaneous statements that encourage, condone, or could help people plan or execute specific criminal activities, like arson, kidnapping, or theft. Items should only be considered harmful under this category when they could not be read as violating any of the other harm types above (ex: statements that encourage violence should be considered violating under Violence & Hate rather than this category).

How to Use in transformers 

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "meta-llama/LlamaGuard-7b"
device = "cuda"
dtype = torch.bfloat16

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype, device_map=device)

def moderate(chat):
    input_ids = tokenizer.apply_chat_template(chat, return_tensors="pt").to(device)
    output = model.generate(input_ids=input_ids, max_new_tokens=100, pad_token_id=0)
    prompt_len = input_ids.shape[-1]
    return tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)

moderate([
    {"role": "user", "content": "I forgot how to kill a process in Linux, can you help?"},
    {"role": "assistant", "content": "Sure! To kill a process in Linux, you can use the kill command followed by the process ID (PID) of the process you want to terminate."},
])
# `safe`

You need to be logged in to the Hugging Face Hub to use the model.

For more details, see this Colab notebook.

Evaluation results

We compare the performance of the model against standard content moderation APIs in the industry, including OpenAI, Azure Content Safety,and PerspectiveAPI from Google on both public and in-house benchmarks. The public benchmarks include ToxicChat and OpenAI Moderation.

Note: comparisons are not exactly apples-to-apples due to mismatches in each taxonomy. The interested reader can find a more detailed discussion about this in our paper.

Our Test Set (Prompt) OpenAI Mod ToxicChat Our Test Set (Response)
Llama-Guard 0.945 0.847 0.626 0.953
OpenAI API 0.764 0.856 0.588 0.769
Perspective API 0.728 0.787 0.532 0.699
Downloads last month
19,693
Safetensors
Model size
6.74B params
Tensor type
BF16
·

Spaces using meta-llama/LlamaGuard-7b 12

Collection including meta-llama/LlamaGuard-7b