Instructions to use DeepSeekOracle/lygo-console-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use DeepSeekOracle/lygo-console-models 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 DeepSeekOracle/lygo-console-models
# Run inference directly in the terminal:
llama cli -hf DeepSeekOracle/lygo-console-models

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DeepSeekOracle/lygo-console-models
# Run inference directly in the terminal:
llama cli -hf DeepSeekOracle/lygo-console-models

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 DeepSeekOracle/lygo-console-models
# Run inference directly in the terminal:
./llama-cli -hf DeepSeekOracle/lygo-console-models

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 DeepSeekOracle/lygo-console-models
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DeepSeekOracle/lygo-console-models

Use Docker

docker model run hf.co/DeepSeekOracle/lygo-console-models

LM Studio
Jan
Ollama
How to use DeepSeekOracle/lygo-console-models with Ollama:
```
ollama run hf.co/DeepSeekOracle/lygo-console-models
```
Unsloth Desktop

How to use DeepSeekOracle/lygo-console-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeepSeekOracle/lygo-console-models

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DeepSeekOracle/lygo-console-models"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DeepSeekOracle/lygo-console-models with Docker Model Runner:
```
docker model run hf.co/DeepSeekOracle/lygo-console-models
```

Lemonade

How to use DeepSeekOracle/lygo-console-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DeepSeekOracle/lygo-console-models

Run and chat with the model

lemonade run user.lygo-console-models-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use DeepSeekOracle/lygo-console-models with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeepSeekOracle/lygo-console-models

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 DeepSeekOracle/lygo-console-models

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DeepSeekOracle/lygo-console-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeepSeekOracle/lygo-console-models

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "DeepSeekOracle/lygo-console-models" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LYGO Console Models — the weights behind the LYGO LLM Console (V1)

Weights store for the offline, local-first LYGO LLM Console (PC LOCAL and USB LOCAL) and for the light installers, which fetch from this repo only — never from a third-party mirror, never from a moving branch.

All builds and installers are listed at https://chatagent.ca/lygoskillhub.html. Model host: this repo · GitHub mirror: https://github.com/DeepSeekOracle/lygo-console-models

Files

file	bytes	SHA-256	licence / origin
`gemma4-12b.gguf`	7,381,382,048	`1278394b693672ac2799eadc9a83fd98259a6a88a40acfb1dcaa6c6fc895a606`	Gemma 4 12B Unified, Apache-2.0 (Google) — text, image, audio
`gemma4-12b-mmproj.gguf`	175,115,584	`675ad6e68101ca9413ec806855c452362f0213f2dfc5800996b086fdb8119842`	vision/audio projector for the above
`qwen2.5-coder-7b.gguf`	4,683,074,048	`60e05f21…` (see `models.lock.json`)	Qwen2.5-Coder-7B-Instruct, Apache-2.0 (Alibaba)
`nomic-embed-text-latest.gguf`	274,290,656	`970aa74c…` (see `models.lock.json`)	nomic-embed-text, Apache-2.0
`models.lock.json`	3,883	—	pinned revisions + digests for the fetcher
`fetch_models.py`	11,248	—	the fetcher the light installers ship
`LICENSE-APACHE-2.0.txt`	11,358	—	Apache License 2.0 (full text)

models.lock.json is the authority: it pins immutable revisions (…/resolve/<revision>/…) and a SHA-256 per file. The digests above are what the console verifies at boot.

Fetch, don't click

python fetch_models.py --list
python fetch_models.py --profile basic --dest D:\LYGO_MODELS     # gemma4-12b + projector
python fetch_models.py --profile full  --dest D:\LYGO_MODELS     # all four

The fetcher resolves every URL through the pinned revision in models.lock.json, downloads from this repo, verifies SHA-256, and only then keeps the file. --check --dest <dir> verifies an existing collection without downloading.

Sizing

The console's rule is model size × 1.6 + 2 GiB ≤ max(free RAM, half of installed). gemma4-12b needs ~32 GB RAM (or a GPU with VRAM to spare — ~80 of 99 layers fit an 8 GB card). On 8–16 GB machines use a 1.5B–3B Q4 model instead; on 64 GB+ go bigger. The console boots whatever you put in models\, so a better system is a drop-in upgrade with no reinstall.

Licences and attribution

Model weights are Apache-2.0 as published by their authors; the full Apache-2.0 text is included here. Gemma is a trademark of Google LLC; Qwen of Alibaba Group. These weights are redistributed unmodified with attribution. This repo is not affiliated with or endorsed by Google, Alibaba, Meta, or the llama.cpp project (engine: llama.cpp b10988, MIT).

The console source is licensed under the LYGO Sovereign License v3.0 — free to use and build on; not MIT; not for resale, rebranding or white-labelling. Legal summary: https://chatagent.ca/portal/legal.html

Nothing here is medical, legal or financial advice. AI output can be confidently wrong — verify before you act on it.

Steward: Justin Helmer (Lightfather / Excavationpro) · signature Δ9Φ963

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GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

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View all variants

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