Instructions to use LLMNavitgat0r/Oraclev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LLMNavitgat0r/Oraclev2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf LLMNavitgat0r/Oraclev2 # Run inference directly in the terminal: llama cli -hf LLMNavitgat0r/Oraclev2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LLMNavitgat0r/Oraclev2 # Run inference directly in the terminal: llama cli -hf LLMNavitgat0r/Oraclev2
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 LLMNavitgat0r/Oraclev2 # Run inference directly in the terminal: ./llama-cli -hf LLMNavitgat0r/Oraclev2
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 LLMNavitgat0r/Oraclev2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf LLMNavitgat0r/Oraclev2
Use Docker
docker model run hf.co/LLMNavitgat0r/Oraclev2
- LM Studio
- Jan
- Ollama
How to use LLMNavitgat0r/Oraclev2 with Ollama:
ollama run hf.co/LLMNavitgat0r/Oraclev2
- Unsloth Studio
How to use LLMNavitgat0r/Oraclev2 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 LLMNavitgat0r/Oraclev2 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 LLMNavitgat0r/Oraclev2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LLMNavitgat0r/Oraclev2 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use LLMNavitgat0r/Oraclev2 with Docker Model Runner:
docker model run hf.co/LLMNavitgat0r/Oraclev2
- Lemonade
How to use LLMNavitgat0r/Oraclev2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LLMNavitgat0r/Oraclev2
Run and chat with the model
lemonade run user.Oraclev2-{{QUANT_TAG}}List all available models
lemonade list
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B pipeline_tag: text-generation
Oracle-LLM-8B-GGUF
This is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Llama-8B, converted to GGUF format (Q4_K_M quantization) for use with LM Studio and llama.cpp.
It’s designed for cybersecurity tasks, specializing in network security, Blue Team/Red Team strategies, and securing operating systems.
Model Details
- Base Model:
deepseek-ai/DeepSeek-R1-Distill-Llama-8B - Fine-Tuning: Trained on the Cybersecurity Bundle and Kali Linux Bundle, with a focus on adversarial tradecraft, penetration testing, and defensive strategies.
- Quantization: Q4_K_M (4-bit) to fit on GPUs with 12GB VRAM (e.g., RTX 4070 Super).
Usage with LM Studio
- Install LM Studio from https://lmstudio.ai/.
- Download
oracle_llm_8b_q4km.gguffrom this repository. - Load the model with 4-bit quantization (Q4_K_M).
- Set the system prompt:
- Repeat with oracle_llm_8b.gguf (Which is Full-Precision Model)
Disclaimer
This Model cannot Generate any Malware for educational or intentional purposes what so ever (It sucks). It can however analyze files. This is more of a lighter and faster reasoning model. This model, Oracle-LLM-8B-GGUF, is provided for educational and research purposes only. The creator is not responsible for any misuse, including the creation, distribution, or use of malware, or any other illegal or harmful activities. Users assume full responsibility for their actions and are advised to use this model at their own discretion, in compliance with all applicable laws and ethical standards.
- Downloads last month
- 40
We're not able to determine the quantization variants.