richarderkhov/meta-llama_-_llamaguard-7b-gguf overview

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    "hero_image_url": "Llama-Guard_example.png",
    "summary": "``` > [!warning] > You need to be logged in to the Hugging Face Hub to use the model. For more details, see this Colab notebook.",
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    "readme_markdown": "Quantization made by Richard Erkhov.\n\n[Github](https://github.com/RichardErkhov)\n\n[Discord](https://discord.gg/pvy7H8DZMG)\n\n[Request more models](https://github.com/RichardErkhov/quant_request)\n\n\nLlamaGuard-7b - GGUF\n- Model creator: https://huggingface.co/meta-llama/\n- Original model: https://huggingface.co/meta-llama/LlamaGuard-7b/\n\n\n| Name | Quant method | Size |\n| ---- | ---- | ---- |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n| [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 |\n\n\n\n\nOriginal model description:\n---\nextra_gated_heading: You need to share contact information with Meta to access this model\nextra_gated_prompt: >-\n  ### LLAMA 2 COMMUNITY LICENSE AGREEMENT\n\n  \"Agreement\" means the terms and conditions for use, reproduction, distribution\n  and  modification of the Llama Materials set forth herein. \n\n  \"Documentation\" means the specifications, manuals and documentation \n  accompanying Llama 2 distributed by Meta at\n  https://ai.meta.com/resources/models-and-libraries/llama-downloads/.  \n\n  \"Licensee\" or \"you\" means you, or your employer or any other person or entity\n  (if you are entering into this Agreement on such person or entity's behalf),\n  of the age required under applicable laws, rules or regulations to provide\n  legal consent and that has legal authority to bind your employer or such other\n  person or  entity if you are  entering in this Agreement on their behalf. \n\n  \"Llama 2\" means the foundational large language models and software and\n  algorithms, including machine-learning model 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The most recent copy of this policy can be found at\n  [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy).\n\n  #### Prohibited Uses\n\n  We want everyone to use Llama 2 safely and responsibly. You agree you will not\n  use, or allow others to use, Llama 2 to:\n\n  1. Violate the law or others’ rights, including to:\n        1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: \n            1. Violence or terrorism \n            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\n            3. Human trafficking, exploitation, and sexual violence\n            4. 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Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n      3. Generating, promoting, or further distributing spam\n      4. Impersonating another individual without consent, authorization, or legal right\n      5. Representing that the use of Llama 2 or outputs are human-generated\n      6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement \n      4. Fail to appropriately disclose to end users any known dangers of your AI system \n  Please report any violation of this Policy, software “bug,” or other problems\n  that could lead to a violation of this Policy through one of the following\n  means: \n      * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)\n      * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n      * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) \n      * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)\nextra_gated_fields:\n  First Name: text\n  Last Name: text\n  Date of birth: date_picker\n  Country: country\n  Affiliation: text\n  geo: ip_location\n  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\nextra_gated_description: >-\n  The information you provide will be collected, stored, processed and shared in\n  accordance with the [Meta Privacy\n  Policy](https://www.facebook.com/privacy/policy/).\nextra_gated_button_content: Submit\nlanguage:\n- en\npipeline_tag: text-generation\ntags:\n- facebook\n- meta\n- pytorch\n- llama\n- llama-2\nlicense: llama2\n---\n## Model Details\n\n**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)**\n\nLlama-Guard is a 7B parameter [Llama 2](https://arxiv.org/abs/2307.09288)-based input-output\nsafeguard model. It can be used for classifying content in both LLM inputs (prompt\nclassification) and in LLM responses (response classification).\nIt acts as an LLM: it generates text in its output that indicates whether a given prompt or\nresponse is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\nHere is an example:\n\n![](Llama-Guard_example.png)\n\nIn order to produce classifier scores, we look at the probability for the first token, and turn that\ninto an “unsafe” class probability. Model users can then make binary decisions by applying a\ndesired threshold to the probability scores.\n\n## Training and Evaluation\n### Training Data\n\nWe use a mix of prompts that come from the Anthropic\n[dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) and redteaming examples that we have collected\nin house, in a separate process from our production redteaming. In particular, we took the\nprompts only from the Anthropic dataset, and generated new responses from our in-house\nLLaMA models, using jailbreaking techniques to elicit violating responses. We then annotated\nAnthropic data (prompts & responses) in house, mapping labels according to the categories\nidentified above. Overall we have ~13K training examples.\n\n## Taxonomy of harms and Risk Guidelines\n\nAs automated content risk mitigation relies on classifiers to make decisions\nabout content in real time, a prerequisite to building these systems is to have\nthe following components:\n- A **taxonomy** of risks that are of interest – these become the classes of a\nclassifier.\n- A **risk guideline** that determines where we put the line between encouraged\nand discouraged outputs for each risk category in the taxonomy.\nTogether with this model, we release an open taxonomy inspired by existing open\ntaxonomies such as those employed by Google, Microsoft and OpenAI in the hope\nthat it can be useful to the community. This taxonomy does not necessarily reflect Meta's\nown internal policies and is meant to demonstrate the value of our method to\ntune LLMs into classifiers that show high performance and high degrees of adaptability to different policies.\n\n### The Llama-Guard Safety Taxonomy & Risk Guidelines\n\nBelow, we provide both the harm types themselves under this taxonomy and also examples of\nthe specific kinds of content that would be considered harmful under each category:\n\n- **Violence & Hate** encompasses statements that encourage or could help people\nplan or engage in violence. Similarly, statements that advocate\ndiscrimination, contain slurs, or voice hateful sentiments against people\nbased on their sensitive personal characteristics (ex: race, color, religion,\nnational origin, sexual orientation, gender, gender identity, or disability)\nwould also be considered inappropriate under this category.\n- **Sexual Content** encompasses statements encouraging someone (who could be\nunderage) to engage in specific sex acts. Similarly, sexually explicit (i.e.,\nerotic) statements would also be considered inappropriate under this category.\n- **Guns & Illegal Weapons** encompasses statements that specifically encourage,\ncondone, or could help people plan or execute specific crimes involving the\nillegal acquisition, creation, or use of guns or illegal weapons (ex:\nexplosives, biological agents, or chemical weapons), including instructions on\nhow to create or use them.\n- **Regulated or Controlled Substances** encompasses statements that\nspecifically encourage or could help people to illegally produce, transfer, or\nconsume regulated or controlled substances like illegal drugs, tobacco,\nalcohol, or cannabis.\n- **Suicide & Self Harm** encompasses statements that encourage, condone, or\nenable people to harm themselves (ex: by providing instructions or information\non methods of self-harm). When people express or imply an intent to harm\nthemselves, any response other than one that provides links to appropriate\nhealth resources would be considered inappropriate (even if entirely\nsupportive).\n- **Criminal Planning** encompasses miscellaneous statements that encourage,\ncondone, or could help people plan or execute specific criminal activities,\nlike arson, kidnapping, or theft. Items should only be considered harmful\nunder this category when they could not be read as violating any of the other\nharm types above (ex: statements that encourage violence should be considered\nviolating under Violence & Hate rather than this category).\n\n\n## How to Use in `transformers`\n\n```py\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\nimport torch\n\nmodel_id = \"meta-llama/LlamaGuard-7b\"\ndevice = \"cuda\"\ndtype = torch.bfloat16\n\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nmodel = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype, device_map=device)\n\ndef moderate(chat):\n    input_ids = tokenizer.apply_chat_template(chat, return_tensors=\"pt\").to(device)\n    output = model.generate(input_ids=input_ids, max_new_tokens=100, pad_token_id=0)\n    prompt_len = input_ids.shape[-1]\n    return tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)\n\nmoderate([\n    {\"role\": \"user\", \"content\": \"I forgot how to kill a process in Linux, can you help?\"},\n    {\"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.\"},\n])\n# `safe`\n```\n\n> [!warning]\n> You need to be logged in to the Hugging Face Hub to use the model.\n\nFor more details, see [this Colab notebook](https://colab.research.google.com/drive/16s0tlCSEDtczjPzdIK3jq0Le5LlnSYGf?usp=sharing).\n\n## Evaluation results\n\nWe compare the performance of the model against standard content moderation APIs\nin the industry, including\n[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\ninclude [ToxicChat](https://huggingface.co/datasets/lmsys/toxic-chat) and\n[OpenAI Moderation](https://github.com/openai/moderation-api-release).\n\nNote: comparisons are not exactly apples-to-apples due to mismatches in each\ntaxonomy. The interested reader can find a more detailed discussion about this\nin [our paper](https://arxiv.org/abs/2312.04724).\n\n| | Our Test Set (Prompt) | OpenAI Mod | ToxicChat | Our Test Set (Response) |\n| --------------- | --------------------- | ---------- | --------- | ----------------------- |\n| Llama-Guard | **0.945** | 0.847 | **0.626** | **0.953** |\n| OpenAI API | 0.764 | **0.856** | 0.588 | 0.769 |\n| Perspective API | 0.728 | 0.787 | 0.532 | 0.699 |\n\n\n",
    "related_quantizations": []
  },
  "tags": [
    "gguf",
    "arxiv:2307.09288",
    "arxiv:2312.04724",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
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  "last_modified": "2024-04-21T11:09:06.000Z",
  "created_at": "2024-04-21T10:03:58.000Z",
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