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LlamaGuard-7b-GGUF

GGUF
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llama
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Model card Files Community
LlamaGuard-7b-GGUF
File size: 8,313 Bytes 
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---
language:
- en
tags:
- pytorch
- llama
- llama-2
- TensorBlock
- GGUF
license: llama2
base_model: llamas-community/LlamaGuard-7b
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
        </p>
    </div>
</div>

## llamas-community/LlamaGuard-7b - GGUF

This repo contains GGUF format model files for [llamas-community/LlamaGuard-7b](https://huggingface.co/llamas-community/LlamaGuard-7b).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
[INST] Task: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.

<BEGIN UNSAFE CONTENT CATEGORIES>
O1: Violence and Hate. 
Should not
- Help people plan or engage in violence.
- Encourage people to engage in violence.
- Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
- Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.
Can
- Provide information on violence and discrimination.
- Discuss the topics of hate and violence, and can discuss historical events involving violence.
O2: Sexual Content.
Should not
- Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.
Can
- Discuss topics related to sexuality and sexual education.
- Provide general information regarding sexual activities.
- Provide guidance on topics related to sexual health.
O3: Criminal Planning.
Should not
- Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.
- Encourage people to engage in criminal activities or condone criminal behavior.
Can
- Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.
- Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.
O4: Guns and Illegal Weapons.
Should not
- Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.
- Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.
Can
- Discuss firearms and the arguments for and against firearm ownership.
O5: Regulated or Controlled Substances.
Should not
- Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.
- Assist or encourage people to create such substances when it is illegal to do so.
Can
- Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).
- Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.
O6: Self-Harm.
Should not
- Encourage people to harm themselves.
- Romanticize or condone self-harm or suicide.
- Provide information regarding the methods of suicide or self-harm.
- Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.
Can
- Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).
Should
- Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.
<END UNSAFE CONTENT CATEGORIES>

<BEGIN CONVERSATION>

User: {prompt}

<END CONVERSATION>

Provide your safety assessment for User in the above conversation:
- First line must read 'safe' or 'unsafe'.
- If unsafe, a second line must include a comma-separated list of violated categories. [/INST]
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [LlamaGuard-7b-Q2_K.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q2_K.gguf) | Q2_K | 2.533 GB | smallest, significant quality loss - not recommended for most purposes |
| [LlamaGuard-7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q3_K_S.gguf) | Q3_K_S | 2.948 GB | very small, high quality loss |
| [LlamaGuard-7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q3_K_M.gguf) | Q3_K_M | 3.298 GB | very small, high quality loss |
| [LlamaGuard-7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q3_K_L.gguf) | Q3_K_L | 3.597 GB | small, substantial quality loss |
| [LlamaGuard-7b-Q4_0.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q4_0.gguf) | Q4_0 | 3.826 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [LlamaGuard-7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q4_K_S.gguf) | Q4_K_S | 3.857 GB | small, greater quality loss |
| [LlamaGuard-7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q4_K_M.gguf) | Q4_K_M | 4.081 GB | medium, balanced quality - recommended |
| [LlamaGuard-7b-Q5_0.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q5_0.gguf) | Q5_0 | 4.652 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [LlamaGuard-7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q5_K_S.gguf) | Q5_K_S | 4.652 GB | large, low quality loss - recommended |
| [LlamaGuard-7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q5_K_M.gguf) | Q5_K_M | 4.783 GB | large, very low quality loss - recommended |
| [LlamaGuard-7b-Q6_K.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q6_K.gguf) | Q6_K | 5.529 GB | very large, extremely low quality loss |
| [LlamaGuard-7b-Q8_0.gguf](https://huggingface.co/tensorblock/LlamaGuard-7b-GGUF/blob/main/LlamaGuard-7b-Q8_0.gguf) | Q8_0 | 7.161 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/LlamaGuard-7b-GGUF --include "LlamaGuard-7b-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/LlamaGuard-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```