How to use from
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 SlayThat/NikiAI-Survival
# Run inference directly in the terminal:
llama cli -hf SlayThat/NikiAI-Survival
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SlayThat/NikiAI-Survival
# Run inference directly in the terminal:
llama cli -hf SlayThat/NikiAI-Survival
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 SlayThat/NikiAI-Survival
# Run inference directly in the terminal:
./llama-cli -hf SlayThat/NikiAI-Survival
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 SlayThat/NikiAI-Survival
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SlayThat/NikiAI-Survival
Use Docker
docker model run hf.co/SlayThat/NikiAI-Survival
Quick Links

NikiAI-Survival (GGUF)

NikiAI-Survival is a lightweight, domain-adapted language model based on meta-llama/Llama-3.2-1B. It is fine-tuned on specialized knowledge covering wilderness survival, first aid, bushcraft, emergency signaling, water purification, and disaster preparedness.


Model Details

  • Developed by: SlayThat
  • Base Model: meta-llama/Llama-3.2-1B
  • Fine-Tuning Framework: Unsloth
  • Format: GGUF (4-bit medium quantization Q4_K_M)
  • Primary Domain: Survival Skills, Wilderness Medicine, Bushcraft, Emergency Preparedness

How to Run Locally

Option 1: LM Studio

  1. Open LM Studio.
  2. Drag and drop the downloaded llama-3.2-1b.Q4_K_M.gguf file directly into the application.
  3. Select the model from the top dropdown menu and start chatting.

Option 2: Ollama

  1. Place the .gguf file in a dedicated folder.
  2. Create a text file named Modelfile in the same directory with this content:
    FROM ./llama-3.2-1b.Q4_K_M.gguf
    PARAMETER stop "<|eot_id|>"
    
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GGUF
Model size
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Architecture
llama
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