Instructions to use JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B 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 JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B 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 JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B # Run inference directly in the terminal: llama cli -hf JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B # Run inference directly in the terminal: llama cli -hf JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
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 JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B # Run inference directly in the terminal: ./llama-cli -hf JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
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 JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B # Run inference directly in the terminal: ./build/bin/llama-cli -hf JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
Use Docker
docker model run hf.co/JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
- LM Studio
- Jan
- vLLM
How to use JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
- Ollama
How to use JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B with Ollama:
ollama run hf.co/JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
- Unsloth Studio
How to use JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B 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 JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B 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 JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B to start chatting
- Docker Model Runner
How to use JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B with Docker Model Runner:
docker model run hf.co/JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
- Lemonade
How to use JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull JeffTheNerdDev96/AURA-Eve-Tactical-Instruct-3.8B
Run and chat with the model
lemonade run user.AURA-Eve-Tactical-Instruct-3.8B-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
AURA-Eve-Tactical-Instruct-3.8B
A.U.R.A. (Adaptive Underworld Recon Array) is an expert shipboard tactical combat AI fine-tuned for EVE Online.
Tuned on top of Phi-4-mini-instruct (3.8B) using Unsloth, this model provides low-latency, tactical ship profiles, EFT fittings, combat threat analysis, module mechanics, and solar system intel formatted as a direct combat copilot.
Model Summary
- Developed by: Jeff (JeffTheNerd96)
- Model Type: Causal Language Model (Fine-Tuned Instruction/Tactical Copilot)
- Base Architecture:
microsoft/Phi-4-mini-instruct(3.8B Parameters) - Quantization: 4-bit Medium K-Quant (
Q4_K_M) viallama.cpp - Context Length: 2,048 tokens
- License: MIT
Training Details
- Dataset: Unified corpus of EVE Online ship profiles, EFT fitting archetypes, module powergrid/CPU statistics, combat ranges, and regional intel.
- Method: 4-bit QLoRA with rank $r=16$ and $\alpha=16$ targeting all linear projection modules (
q,k,v,o,gate,up,down). - Precision: Mixed precision training via Unsloth on NVIDIA T4 hardware.
Prompt Template
The model uses the standard Phi-4 / ChatML template:
<|im_start|>system
You are A.U.R.A. (Adaptive Underworld Recon Array), the elite tactical shipboard combat AI of the Angel Cartel in EVE Online. Provide concise, high-impact tactical advice.<|im_end|>
<|im_start|>user
Tactical analysis and profile for Cynabal (Cruiser)?<|im_end|>
<|im_start|>assistant
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We're not able to determine the quantization variants.