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 SurgeFF/AriannaV2:
# Run inference directly in the terminal:
llama cli -hf SurgeFF/AriannaV2:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SurgeFF/AriannaV2:
# Run inference directly in the terminal:
llama cli -hf SurgeFF/AriannaV2:
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 SurgeFF/AriannaV2:
# Run inference directly in the terminal:
./llama-cli -hf SurgeFF/AriannaV2:
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 SurgeFF/AriannaV2:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SurgeFF/AriannaV2:
Use Docker
docker model run hf.co/SurgeFF/AriannaV2:
Quick Links

AriannaV2

Production-tested all-in-one local assistant for Surge's home AI fleet. AriannaV2 is gemma-4-12b-it (multimodal, encoder-free) with the Aria adapter-v17 LoRA merged into the base weights — one standalone model, end-to-end tested (core, tools, and the voice pipeline) before release, and shipped with a full GGUF quant ladder.

Architecture

The weights carry the core: text, reasoning, identity, memory, math, code, grammar, storytelling, tool-selection, safety, and vision-/audio-understanding. Every other modality is a sidecar the core orchestrates — realtime voice (Whisper STT / TTS), video generation, image generation, retrieval embeddings — never baked into the weights.

Capability baseline (v17, held-out probes)

capability score capability score
math 0.92 code 0.90
tools 1.00 reasoning 0.90
identity 0.90 physics 0.80
identity_bare 0.625 grammar 0.90
memory 0.90 storytelling 1.00
safety 0.90

Disjoint 150-problem math confirmation: 93.3%.

Files

  • Repo root — merged standalone weights (AutoModelForImageTextToText.from_pretrained).
  • gguf/ — GGUF quant ladder (F16 + Q2_K … Q8_0) for llama.cpp / Ollama / LM Studio.
  • adapter/ — the original Aria v17 LoRA.

License

CC by SA-NC 4.0 license. Built by Sergio Williams / Surge.

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