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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+LlamaGuard-7b - GGUF
+- Model creator: https://huggingface.co/meta-llama/
+- Original model: https://huggingface.co/meta-llama/LlamaGuard-7b/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [LlamaGuard-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q2_K.gguf) | Q2_K | 2.36GB |
+| [LlamaGuard-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
+| [LlamaGuard-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.IQ3_S.gguf) | IQ3_S | 2.75GB |
+| [LlamaGuard-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
+| [LlamaGuard-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.IQ3_M.gguf) | IQ3_M | 2.9GB |
+| [LlamaGuard-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q3_K.gguf) | Q3_K | 3.07GB |
+| [LlamaGuard-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
+| [LlamaGuard-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
+| [LlamaGuard-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
+| [LlamaGuard-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q4_0.gguf) | Q4_0 | 3.56GB |
+| [LlamaGuard-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
+| [LlamaGuard-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
+| [LlamaGuard-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q4_K.gguf) | Q4_K | 3.8GB |
+| [LlamaGuard-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
+| [LlamaGuard-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q4_1.gguf) | Q4_1 | 3.95GB |
+| [LlamaGuard-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q5_0.gguf) | Q5_0 | 4.33GB |
+| [LlamaGuard-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
+| [LlamaGuard-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q5_K.gguf) | Q5_K | 4.45GB |
+| [LlamaGuard-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
+| [LlamaGuard-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q5_1.gguf) | Q5_1 | 4.72GB |
+| [LlamaGuard-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/meta-llama_-_LlamaGuard-7b-gguf/blob/main/LlamaGuard-7b.Q6_K.gguf) | Q6_K | 5.15GB |
+
+
+
+
+Original model description:
+---
+extra_gated_heading: You need to share contact information with Meta to access this model
+extra_gated_prompt: >-
+  ### 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
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+
+  "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
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+  https://ai.meta.com/llama/use-policy), which is hereby incorporated by
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+
+  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 
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+
+
+  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
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+  3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE  LLAMA
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+  5. Intellectual Property.
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+  a. No trademark licenses are granted under this Agreement, and in  connection
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+
+  7. Governing Law and Jurisdiction. This Agreement will be governed and 
+  construed under the laws of the State of California without regard to choice
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+  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](http://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 
+      4. 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: 
+      * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
+      * 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: [[email protected]](mailto:[email protected])
+extra_gated_fields:
+  First Name: text
+  Last Name: text
+  Date of birth: date_picker
+  Country: country
+  Affiliation: text
+  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
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- facebook
+- meta
+- pytorch
+- llama
+- llama-2
+license: llama2
+---
+## 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](https://huggingface.co/meta-llama/LlamaGuard-7b/discussions/6)**
+
+Llama-Guard is a 7B parameter [Llama 2](https://arxiv.org/abs/2307.09288)-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:
+
+![](Llama-Guard_example.png)
+
+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](https://huggingface.co/datasets/Anthropic/hh-rlhf) 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`
+
+```py
+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`
+```
+
+> [!warning]
+> You need to be logged in to the Hugging Face Hub to use the model.
+
+For more details, see [this Colab notebook](https://colab.research.google.com/drive/16s0tlCSEDtczjPzdIK3jq0Le5LlnSYGf?usp=sharing).
+
+## Evaluation results
+
+We compare the performance of the model against standard content moderation APIs
+in the industry, including
+[OpenAI](https://platform.openai.com/docs/guides/moderation/overview), [Azure Content Safety](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/harm-categories),and [PerspectiveAPI](https://developers.perspectiveapi.com/s/about-the-api-attributes-and-languages?language=en_US) from Google on both public and in-house benchmarks. The public benchmarks
+include [ToxicChat](https://huggingface.co/datasets/lmsys/toxic-chat) and
+[OpenAI Moderation](https://github.com/openai/moderation-api-release).
+
+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](https://arxiv.org/abs/2312.04724).
+
+| | 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 |
+
+