Instructions to use tensorblock/LlamaGuard-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tensorblock/LlamaGuard-7b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/LlamaGuard-7b-GGUF", filename="LlamaGuard-7b-Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use tensorblock/LlamaGuard-7b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/LlamaGuard-7b-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/LlamaGuard-7b-GGUF with Ollama:
ollama run hf.co/tensorblock/LlamaGuard-7b-GGUF:Q2_K
- Unsloth Studio new
How to use tensorblock/LlamaGuard-7b-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tensorblock/LlamaGuard-7b-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tensorblock/LlamaGuard-7b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tensorblock/LlamaGuard-7b-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/LlamaGuard-7b-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/LlamaGuard-7b-GGUF:Q2_K
- Lemonade
How to use tensorblock/LlamaGuard-7b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/LlamaGuard-7b-GGUF:Q2_K
Run and chat with the model
lemonade run user.LlamaGuard-7b-GGUF-Q2_K
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K# Run inference directly in the terminal:
llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_KUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K# Run inference directly in the terminal:
./llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_KBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_KUse Docker
docker model run hf.co/tensorblock/LlamaGuard-7b-GGUF:Q2_K
llamas-community/LlamaGuard-7b - GGUF
This repo contains GGUF format model files for llamas-community/LlamaGuard-7b.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.
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[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 | Q2_K | 2.533 GB | smallest, significant quality loss - not recommended for most purposes |
| LlamaGuard-7b-Q3_K_S.gguf | Q3_K_S | 2.948 GB | very small, high quality loss |
| LlamaGuard-7b-Q3_K_M.gguf | Q3_K_M | 3.298 GB | very small, high quality loss |
| LlamaGuard-7b-Q3_K_L.gguf | Q3_K_L | 3.597 GB | small, substantial quality loss |
| 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 | Q4_K_S | 3.857 GB | small, greater quality loss |
| LlamaGuard-7b-Q4_K_M.gguf | Q4_K_M | 4.081 GB | medium, balanced quality - recommended |
| 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 | Q5_K_S | 4.652 GB | large, low quality loss - recommended |
| LlamaGuard-7b-Q5_K_M.gguf | Q5_K_M | 4.783 GB | large, very low quality loss - recommended |
| LlamaGuard-7b-Q6_K.gguf | Q6_K | 5.529 GB | very large, extremely low quality loss |
| 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
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
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:
huggingface-cli download tensorblock/LlamaGuard-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
- Downloads last month
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2-bit
Model tree for tensorblock/LlamaGuard-7b-GGUF
Base model
llamas-community/LlamaGuard-7b


Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K# Run inference directly in the terminal: llama-cli -hf tensorblock/LlamaGuard-7b-GGUF:Q2_K