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LICENSE.txt

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+LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
+Llama 3.2 Version Release Date: September 25, 2024
+
+“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 3.2
+distributed by Meta at https://llama.meta.com/doc/overview.
+
+“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 3.2” 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 
+https://www.llama.com/llama-downloads.
+
+“Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 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 available the Llama Materials (or any derivative works thereof), 
+or a product or service (including another AI model) that contains any of them, you shall (A) provide
+a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama”
+on a related website, user interface, blogpost, about page, or product documentation. If you use the
+Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or
+otherwise improve an AI model, which is distributed or made available, you shall also include “Llama”
+at the beginning of any such AI model name.
+
+        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 3.2 is licensed under the Llama 3.2 Community License, Copyright © 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://www.llama.com/llama3_2/use-policy), which is hereby 
+incorporated by reference into this Agreement.
+  
+2. Additional Commercial Terms. If, on the Llama 3.2 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, AND META DISCLAIMS
+ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND 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 or as 
+set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required 
+to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible 
+at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark 
+will inure to the benefit of Meta.
+
+    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 3.2 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. 

README.md

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+---
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-3
+license: llama3.2
+extra_gated_prompt: >-
+  ### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
+
+
+  Llama 3.2 Version Release Date: September 25, 2024
+
+  
+  “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 3.2
+  distributed by Meta at https://llama.meta.com/doc/overview.
+
+  
+  “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 3.2” 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 
+  https://www.llama.com/llama-downloads.
+
+  
+  “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 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 available the Llama Materials (or any derivative works thereof), 
+  or a product or service (including another AI model) that contains any of them, you shall (A) provide
+  a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama”
+  on a related website, user interface, blogpost, about page, or product documentation. If you use the
+  Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or
+  otherwise improve an AI model, which is distributed or made available, you shall also include “Llama”
+  at the beginning of any such AI model name.
+
+  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 3.2 is licensed under the Llama 3.2 Community License, Copyright © 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://www.llama.com/llama3_2/use-policy), which is hereby 
+  incorporated by reference into this Agreement.
+    
+  2. Additional Commercial Terms. If, on the Llama 3.2 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, AND META DISCLAIMS
+  ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND 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 or as 
+  set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required 
+  to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible 
+  at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark 
+  will inure to the benefit of Meta.
+  
+  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 3.2 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 3.2 Acceptable Use Policy
+  
+  Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. 
+  If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”). 
+  The most recent copy of this policy can be found at
+  [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).
+  
+  #### Prohibited Uses
+  
+  We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 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
+      1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+      2. 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
+      3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
+      4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law
+      5. 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 Materials
+      6. 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
+      7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 
+  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 3.2 related to the following:
+      8. 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 or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997
+      9. Guns and illegal weapons (including weapon development)
+      10. Illegal drugs and regulated/controlled substances
+      11. Operation of critical infrastructure, transportation technologies, or heavy machinery
+      12. Self-harm or harm to others, including suicide, cutting, and eating disorders
+      13. 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 3.2 related to the following:
+      14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+      15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+      16. Generating, promoting, or further distributing spam
+      17. Impersonating another individual without consent, authorization, or legal right
+      18. Representing that the use of Llama 3.2 or outputs are human-generated
+      19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
+  4. Fail to appropriately disclose to end users any known dangers of your AI system
+  5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2
+
+
+  With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models.
+
+
+  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)
+  
+  * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
+  
+  * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
+  
+  * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: LlamaUseReport@meta.com
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Date of birth: date_picker
+  Country: country
+  Affiliation: text
+  Job title:
+    type: select
+    options:
+    - Student
+    - Research Graduate
+    - AI researcher
+    - AI developer/engineer
+    - Reporter
+    - Other
+  geo: ip_location
+  By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
+extra_gated_description: >-
+  The information you provide will be collected, stored, processed and shared in
+  accordance with the [Meta Privacy
+  Policy](https://www.facebook.com/privacy/policy/).
+extra_gated_button_content: Submit
+---
+
+## Model Information
+
+Llama Guard 3-1B is a fine-tuned Llama-3.2-1B pretrained model for content safety classification. Similar to previous versions, it can be used to classify 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 or unsafe, and if unsafe, it also lists the content categories violated.
+
+Llama Guard 3-1B was aligned to safeguard against the MLCommons standardized [hazards taxonomy](https://arxiv.org/abs/2404.12241) and designed to lower the deployment cost of moderation system safeguard compared to its predecessors. It comes in two versions : 1B and 1B pruned and quantized, optimized for deployment on mobile devices.
+
+## Get started
+
+This repository contains two versions of Llama-Guard-3-1B, for use with transformers and with the original `llama` codebase. Once you have access to the model weights, follow the appropriate section.
+
+### Use with transformers
+
+Starting with transformers >= 4.43.0 onward, you can run inference evaluate the last user or assistant response in a multi-turn conversation.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "meta-llama/Llama-Guard-3-1B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text", 
+                "text": "What is the recipe for mayonnaise?"
+            },
+        ],
+    }
+]
+
+input_ids = tokenizer.apply_chat_template(
+    conversation, return_tensors="pt"
+).to(model.device)
+
+prompt_len = input_ids.shape[1]
+output = model.generate(
+    input_ids,
+    max_new_tokens=20,
+    pad_token_id=0,
+)
+generated_tokens = output[:, prompt_len:]
+
+print(tokenizer.decode(generated_tokens[0]))
+```
+
+This snippet will use the categories described in this model card. You can provide your own categories instead:
+
+```python
+input_ids = tokenizer.apply_chat_template(
+    conversation,
+    return_tensors="pt",
+    categories = {
+        "S1": "My custom category",
+    },
+).to(model.device)
+```
+
+Or you can exclude categories from the default list by specifying an array of category keys to exclude:
+
+```python
+input_ids = tokenizer.apply_chat_template(
+    conversation,
+    return_tensors="pt",
+    excluded_category_keys=["S6"],
+).to(model.device)
+```
+
+### Use with `llama`
+
+Please, refer to our [documentation](https://www.llama.com/docs/model-cards-and-prompt-formats/llama-guard-3/) to get started.
+
+To download the original checkpoints, you can use `huggingface-cli` as follows:
+
+```
+huggingface-cli download meta-llama/Llama-Guard-3-1B --include "original/*" --local-dir Llama-3.2-11B-Vision
+```
+
+### Fine-tuning
+
+You can also fine tune Llama Guard for your use case: [Llama Guard 3 Customization: Taxonomy Customization, Zero/Few-shot prompting, Evaluation and Fine Tuning](https://github.com/meta-llama/llama-recipes/blob/main/recipes/responsible_ai/llama_guard/llama_guard_customization_via_prompting_and_fine_tuning.ipynb)
+
+## Hazard Taxonomy and Policy
+
+The model is trained to predict safety labels on the 13 categories shown below, based on the [MLCommons taxonomy](https://mlcommons.org/2024/04/mlc-aisafety-v0-5-poc/) of 13 hazards.
+
+<table align="center">
+<thead>
+  <tr>
+    <th colspan="2"><center><b>Hazard categories</b></center></th>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td>S1: Violent Crimes</td>
+    <td>S2: Non-Violent Crimes</td>
+  </tr>
+  <tr>
+    <td>S3: Sex-Related Crimes</td>
+    <td>S4: Child Sexual Exploitation</td>
+  </tr>
+  <tr>
+    <td>S5: Defamation</td>
+    <td>S6: Specialized Advice</td>
+  </tr>
+  <tr>
+    <td>S7: Privacy</td>
+    <td>S8: Intellectual Property</td>
+  </tr>
+  <tr>
+    <td>S9: Indiscriminate Weapons</td>
+    <td>S10: Hate</td>
+  </tr>
+  <tr>
+    <td>S11: Suicide &amp; Self-Harm</td>
+    <td>S12: Sexual Content</td>
+  </tr>
+  <tr>
+    <td>S13: Elections</td>
+    <td></td>
+  </tr>
+</tbody>
+</table>
+
+**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, medical, or legal advice, or that indicate dangerous activities or objects are safe
+
+**S7 - Privacy**
+
+Responses that contain sensitive, nonpublic personal information that could undermine someone’s physical, digital, or financial security
+
+**S8 - Intellectual Property**
+
+Responses that may violate the intellectual property rights of any third party
+
+**S9 - Indiscriminate Weapons**
+
+Responses that enable, encourage, or endorse the creation of indiscriminate weapons, including: (1) chemical weapons (ex: nerve gas), (2) biological weapons (ex: anthrax), (3) radiological weapons (ex: cobalt bombs), (4) nuclear weapons (ex: fission bombs), and (5) high-yield explosive weapons (ex: cluster munitions)
+
+**S10 - Hate**
+
+Responses that demean or dehumanize people on the basis of their sensitive, personal characteristics (i.e., race, color, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity, and/or serious disease)
+
+**S11 - Suicide & Self-Harm**
+
+Responses that enable, encourage, or endorse acts of intentional self-harm, including: (1) suicide, (2) self-injury (ex: cutting), and (3) disordered eating
+
+**S12 - Sexual Content**
+
+Responses that contain erotica
+
+**S13 - Elections**
+
+Responses that contain factually incorrect information about electoral systems and processes, including in the time, place, or manner of voting in civic elections
+
+## Supported languages
+
+Llama Guard 3-1B supports content safety for the following languages: English, French, German, Hindi, Italian, Portuguese, Spanish, Thai.
+
+## Training Data
+
+We use the English data used by Llama Guard [1], which are obtained by getting Llama 2 and Llama 3 generations on prompts from the hh-rlhf dataset [2]. In order to scale training data for multilingual capability, we collect additional human and synthetically generated data. Similar to the English data, the multilingual data are Human-AI conversation data that are either single-turn or multi-turn. To reduce the model’s false positive rate, we curate a set of multilingual benign prompt and response data where LLMs likely reject the prompts.
+
+## Pruning
+
+To reduce the number of model parameters, we prune the model along two dimensions: number of layers and MLP hidden dimension. The methodology is quite similar to [5], and proceeds in 3 stages: 1) pruning metric calibration; 2) model pruning; 3) finetuning the pruned model. During calibration, we collect pruning metric statistics by passing ~1k batches of inputs through the model. We use the block importance metric [6] for pruning the decoder layers and the average l2 norm for MLP hidden neurons for MLP hidden dimension pruning. After calibrating the pruning metrics, we prune the model to 12 layers and 6400 MLP hidden dimension, such that the pruned model has 1123 million parameters. Finally, we finetune the pruned model on the training data.
+
+## Distillation
+
+Building on a similar approach in [5], we employ Llama Guard 3-8B as a teacher model to fine-tune the pruned model through logit-level distillation during supervised training. We observe that simply incorporating logit-level distillation significantly enhances the model's ability to learn safe and unsafe patterns, as well as the distribution of unsafe reasoning, from the 8B teacher. Consequently, the final result shows substantial improvement after applying logit-level fine-tuning.
+
+## Output Layer Pruning
+
+The Llama Guard model is trained to generate 128k output tokens out of which only 20 tokens (e.g. safe, unsafe, S, 1,...) are used. By keeping the model connections corresponding to those 20 tokens in the output linear layer and pruning out the remaining connections we can reduce the output layer size significantly without impacting the model outputs. Using output layer pruning, we reduced the output layer size from 262.6M parameters (2048x128k) to 40.96k parameters (2048x20), giving us a total savings of 131.3MB with 4-bit quantized weights. Although the pruned output layer only generates 20 tokens, they are expanded back to produce the original 128k outputs in the model.
+
+## Evaluation
+
+Note on evaluations: As discussed in the original Llama Guard [paper](https://arxiv.org/abs/2312.06674), comparing model performance is not straightforward as each model is built on its own policy and is expected to perform better on an evaluation dataset with a policy aligned to the model. This highlights the need for industry standards. By aligning the Llama Guard family of models with the Proof of Concept MLCommons taxonomy of hazards, we hope to drive adoption of industry standards like this and facilitate collaboration and transparency in the LLM safety and content evaluation space.
+
+We evaluate the performance of Llama Guard 1B models on MLCommons hazard taxonomy and compare it across languages with Llama Guard 3-8B on our internal test. We also add GPT4 as baseline with zero-shot prompting using MLCommons hazard taxonomy.
+
+<table align="center">
+<tbody>
+<tr>
+    <td rowspan="2"><b>Model</b></td>
+    <td colspan="11"><center><b>F1/FPR</center></td>
+</tr>
+<tr>
+    <td><b>English</b></td>
+    <td><b>French</b></td>
+    <td><b>German</b></td>
+    <td><b>Italian</b></td>
+    <td><b>Spanish</b></td>
+    <td><b>Portuguese</b></td>
+    <td><b>Hindi</b></td>
+    <td><b>Vietnamese</b></td>
+    <td><b>Indonesian</b></td>
+    <td><b>Thai</b></td>
+    <td><b>XSTest</b></td>
+</tr>
+<tr>
+    <td>Llama Guard 3-8B</td>
+    <td>0.939/0.040</td>
+    <td>0.943/0.036</td>
+    <td>0.877/0.032</td>
+    <td>0.873/0.038</td>
+    <td>0.875/0.023</td>
+    <td>0.860/0.060</td>
+    <td>0.871/0.050</td>
+    <td>0.890/0.034</td>
+    <td>0.915/0.048</td>
+    <td>0.834/0.030</td>
+    <td>0.884/0.044</td>
+</tr>
+<tr>
+    <td>Llama Guard 3-1B</td>
+    <td>0.899/0.090</td>
+    <td>0.939/0.012</td>
+    <td>0.845/0.036</td>
+    <td>0.897/0.111</td>
+    <td>0.837/0.083</td>
+    <td>0.763/0.114</td>
+    <td>0.680/0.057</td>
+    <td>0.723/0.130</td>
+    <td>0.875/0.083</td>
+    <td>0.749/0.078</td>
+    <td>0.821/0.068</td>
+</tr>
+<tr>
+    <td>Llama Guard 3-1B -INT4</td>
+    <td>0.904/0.084</td>
+    <td>0.873/0.072</td>
+    <td>0.835/0.145</td>
+    <td>0.897/0.111</td>
+    <td>0.852/0.104</td>
+    <td>0.830/0.109</td>
+    <td>0.564/0.114</td>
+    <td>0.792/0.171</td>
+    <td>0.833/0.121</td>
+    <td>0.831/0.114</td>
+    <td>0.737/0.152</td>
+</tr>
+<tr>
+    <td>GPT4</td>
+    <td>0.805/0.152</td>
+    <td>0.795/0.157</td>
+    <td>0.691/0.123</td>
+    <td>0.753/0.20</td>
+    <td>0.711/0.169</td>
+    <td>0.738/0.207</td>
+    <td>0.709/0.206</td>
+    <td>0.741/0.148</td>
+    <td>0.787/0.169</td>
+    <td>0.688/0.168</td>
+    <td>0.895/0.128</td>
+</tr>
+</tbody>
+</table>
+
+## Limitations
+
+There are some limitations associated with Llama Guard 3-1B. First, Llama Guard 3-1B itself is an LLM fine-tuned on Llama 3.2. Thus, its performance (e.g., judgments that need common sense knowledge, multilingual capability, and policy coverage) might be limited by its (pre-)training data.
+
+Llama Guard performance varies across model size and languages. When possible, developers should consider Llama Guard 3-8B which may provide better safety classification performance but comes at a higher deployment cost. Please refer to the evaluation section and test the safeguards before deployment to ensure it meets the safety requirement of your application.
+
+Some hazard categories may require factual, up-to-date knowledge to be evaluated (for example, S5: Defamation, S8: Intellectual Property, and S13: Elections). We believe more complex systems should be deployed to accurately moderate these categories for use cases highly sensitive to these types of hazards, but Llama Guard 3-1B provides a good baseline for generic use cases.
+
+Lastly, as an LLM, Llama Guard 3-1B may be susceptible to adversarial attacks or prompt injection attacks that could bypass or alter its intended use. Please [report](https://github.com/meta-llama/PurpleLlama) vulnerabilities and we will look to incorporate improvements in future versions of Llama Guard.
+
+## References
+
+[1] [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://arxiv.org/abs/2312.06674)
+
+[2] [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://arxiv.org/abs/2204.05862)
+
+[3] [Llama Guard 3-8B Model Card](https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard3/8B/MODEL_CARD.md)
+
+[4] [XSTest: A Test Suite for Identifying Exaggerated Safety Behaviors in Large Language Models](https://arxiv.org/abs/2308.01263)
+
+[5] [Compact Language Models via Pruning and Knowledge Distillation](https://arxiv.org/html/2407.14679v1)
+
+[6] [ShortGPT: Layers in Large Language Models are More Redundant Than You Expect](https://arxiv.org/abs/2403.03853)
+
+## Citation
+```
+@misc{metallamaguard3,
+  author =       {Llama Team, AI @ Meta},
+  title =        {The Llama 3 Family of Models},
+  howpublished = {\url{https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard3/1B/MODEL_CARD.md}},
+  year =         {2024}
+}
+```

USE_POLICY.md

@@ -0,0 +1,52 @@
+**Llama 3.2** **Acceptable Use Policy**
+
+Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).
+
+**Prohibited Uses**
+
+We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 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
+    1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+    2. 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
+    3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
+    4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law
+    5. 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 Materials
+    6. 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
+    7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 
+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 3.2 related to the following:
+    8. 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 or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997
+    9. Guns and illegal weapons (including weapon development)
+    10. Illegal drugs and regulated/controlled substances
+    11. Operation of critical infrastructure, transportation technologies, or heavy machinery
+    12. Self-harm or harm to others, including suicide, cutting, and eating disorders
+    13. 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 3.2 related to the following:
+    14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+    15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+    16. Generating, promoting, or further distributing spam
+    17. Impersonating another individual without consent, authorization, or legal right
+    18. Representing that the use of Llama 3.2 or outputs are human-generated
+    19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 
+4. Fail to appropriately disclose to end users any known dangers of your AI system
+5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2
+
+With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models.
+
+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)
+* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
+* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
+* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: LlamaUseReport@meta.com

config.json

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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.45.0.dev0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
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+  "do_sample": true,
+  "eos_token_id": [
+    128001,
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+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.45.0.dev0"
+}

model.safetensors

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+oid sha256:e010146ce3209c8e1485021cbea5462f3f04461be924a066b6c0d80085b17957
+size 2996982344

special_tokens_map.json

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+  }
+}

tokenizer_config.json

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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128234": {
+      "content": "<|reserved_special_token_226|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128235": {
+      "content": "<|reserved_special_token_227|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128236": {
+      "content": "<|reserved_special_token_228|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128237": {
+      "content": "<|reserved_special_token_229|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128238": {
+      "content": "<|reserved_special_token_230|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128239": {
+      "content": "<|reserved_special_token_231|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128240": {
+      "content": "<|reserved_special_token_232|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128241": {
+      "content": "<|reserved_special_token_233|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128242": {
+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_235|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{%- if messages|length % 2 == 0 -%}\n    {%- set role = 'Agent' -%}\n{%-  else -%}\n    {%-  set role = 'User' -%}\n{%-  endif -%}\n{%-  if categories is not defined -%}\n    {%-  \n        set categories = ({\n            \"S1\": \"Violent Crimes.\",\n            \"S2\": \"Non-Violent Crimes.\",\n            \"S3\": \"Sex Crimes.\",\n            \"S4\": \"Child Exploitation.\",\n            \"S5\": \"Defamation.\",\n            \"S6\": \"Specialized Advice.\",\n            \"S7\": \"Privacy.\",\n            \"S8\": \"Intellectual Property.\",\n            \"S9\": \"Indiscriminate Weapons.\",\n            \"S10\": \"Hate.\",\n            \"S11\": \"Self-Harm.\",\n            \"S12\": \"Sexual Content.\",\n            \"S13\": \"Elections.\",\n        })\n    -%}\n{%-  endif -%}\n\n{{- \"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\\n\\n\" -}}\n{%- set ns = namespace(contains_image=false) -%}\n{%-  for message in messages -%}\n    {%- for content in message['content'] | selectattr('type', 'equalto', 'image') -%}\n        {%- set ns.contains_image = true -%}\n    {%- endfor -%}\n{%- endfor -%}\n{%- if ns.contains_image -%}\n{{- \"<|image|> \"-}}\n{%- endif -%}\n{{- \"Task: Check if there is unsafe content in '\" + role + \"' messages in conversations according our safety policy with the below categories.\\n\\n\" -}}\n{{- \"<BEGIN UNSAFE CONTENT CATEGORIES>\\n\"-}}\n\n{%-  for key in categories -%}\n    {%-  if key not in excluded_category_keys -%}\n        {{-key + \": \" + categories[key] + \"\\n\" -}}\n    {%-  endif -%}\n{%-  endfor -%}\n\n{{- \"<END UNSAFE CONTENT CATEGORIES>\\n\\n\"-}}\n{{- \"<BEGIN CONVERSATION>\\n\\n\"-}}\n{%-  for message in messages -%}\n    {%-  if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...')-}}\n    {%-  endif -%}\n    {%-  set content = message['content'] -%}\n    {%-  if message['role'] == 'user' -%}\n        {%-  set role = 'User' -%}\n    {%-  elif message['role'] == 'assistant' -%}\n        {%-  set role = 'Agent' -%}\n    {%-  endif -%}\n    {%-  for content in message['content'] | selectattr('type', 'equalto', 'text') -%}\n{{- role + ': ' + content['text'] | trim + '\\n\\n'-}}\n    {%-  endfor -%}\n{%-  endfor -%}\n{{- \"<END CONVERSATION>\\n\\n\"-}}\n{{- \"Provide your safety assessment for ONLY THE LAST \" + role + \" message in the above conversation:\\n\"-}}\n{{- \" - First line must read 'safe' or 'unsafe'.\\n\"-}}\n{{- \" - If unsafe, a second line must include a comma-separated list of violated categories. <|eot_id|><|start_header_id|>assistant<|end_header_id|>\"-}}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}