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 / README.md

DeepSeekOracle

Add model card: provenance, digests, licences, fetch instructions

9706c43 verified about 1 month ago

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---
license: apache-2.0
library_name: gguf
tags:
  - gguf
  - llama.cpp
  - local
  - vision
  - audio
  - offline
---
# 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`

	---
	license: apache-2.0
	library_name: gguf
	tags:
	- gguf
	- llama.cpp
	- local
	- vision
	- audio
	- offline
	---

	# 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`