Instructions to use krgl/Llama-Primus-Base_8bit-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krgl/Llama-Primus-Base_8bit-gguf with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("krgl/Llama-Primus-Base_8bit-gguf", dtype="auto") - llama-cpp-python
How to use krgl/Llama-Primus-Base_8bit-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="krgl/Llama-Primus-Base_8bit-gguf", filename="Llama-Primus-Base-8Bit.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 krgl/Llama-Primus-Base_8bit-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf krgl/Llama-Primus-Base_8bit-gguf # Run inference directly in the terminal: llama-cli -hf krgl/Llama-Primus-Base_8bit-gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf krgl/Llama-Primus-Base_8bit-gguf # Run inference directly in the terminal: llama-cli -hf krgl/Llama-Primus-Base_8bit-gguf
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 krgl/Llama-Primus-Base_8bit-gguf # Run inference directly in the terminal: ./llama-cli -hf krgl/Llama-Primus-Base_8bit-gguf
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 krgl/Llama-Primus-Base_8bit-gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf krgl/Llama-Primus-Base_8bit-gguf
Use Docker
docker model run hf.co/krgl/Llama-Primus-Base_8bit-gguf
- LM Studio
- Jan
- Ollama
How to use krgl/Llama-Primus-Base_8bit-gguf with Ollama:
ollama run hf.co/krgl/Llama-Primus-Base_8bit-gguf
- Unsloth Studio new
How to use krgl/Llama-Primus-Base_8bit-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 krgl/Llama-Primus-Base_8bit-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 krgl/Llama-Primus-Base_8bit-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for krgl/Llama-Primus-Base_8bit-gguf to start chatting
- Pi new
How to use krgl/Llama-Primus-Base_8bit-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf krgl/Llama-Primus-Base_8bit-gguf
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "krgl/Llama-Primus-Base_8bit-gguf" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use krgl/Llama-Primus-Base_8bit-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf krgl/Llama-Primus-Base_8bit-gguf
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default krgl/Llama-Primus-Base_8bit-gguf
Run Hermes
hermes
- Docker Model Runner
How to use krgl/Llama-Primus-Base_8bit-gguf with Docker Model Runner:
docker model run hf.co/krgl/Llama-Primus-Base_8bit-gguf
- Lemonade
How to use krgl/Llama-Primus-Base_8bit-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull krgl/Llama-Primus-Base_8bit-gguf
Run and chat with the model
lemonade run user.Llama-Primus-Base_8bit-gguf-{{QUANT_TAG}}List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Model Card for 8Bit GGUF version of TrendMicro-Llama-Primus-Base-8bit-gguf
This model is a 8bit Quantized GGUF model of trendmicro-ailab/Llama-Primus-Base For original model and documentation visit
https://huggingface.co/trendmicro-ailab/Llama-Primus-Base
Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
TL;DR: Llama-Primus-Base is a foundation model based on Llama-3.1-8B-Instruct, continually pre-trained on Primus-Seed (0.2B) and Primus-FineWeb (2.57B). Primus-Seed is a high-quality, manually curated cybersecurity text dataset, while Primus-FineWeb consists of cybersecurity texts filtered from FineWeb, a refined version of Common Crawl. By pretraining on such a large-scale cybersecurity corpus, it achieves a 🚀15.88% improvement in aggregated scores across multiple cybersecurity benchmarks, demonstrating the effectiveness of cybersecurity-specific pretraining.
🔥 For more details, please refer to the paper: [📄Paper].
License
This model is based on the MIT license, but you must also comply with the Llama 3.1 Community License Agreement.
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Model tree for krgl/Llama-Primus-Base_8bit-gguf
Base model
meta-llama/Llama-3.1-8B
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="krgl/Llama-Primus-Base_8bit-gguf", filename="Llama-Primus-Base-8Bit.gguf", )