Instructions to use Geneziz/laya-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Geneziz/laya-1.7b 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 Geneziz/laya-1.7b
# Run inference directly in the terminal:
llama cli -hf Geneziz/laya-1.7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Geneziz/laya-1.7b
# Run inference directly in the terminal:
llama cli -hf Geneziz/laya-1.7b

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 Geneziz/laya-1.7b
# Run inference directly in the terminal:
./llama-cli -hf Geneziz/laya-1.7b

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 Geneziz/laya-1.7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Geneziz/laya-1.7b

Use Docker

docker model run hf.co/Geneziz/laya-1.7b

LM Studio
Jan
Ollama
How to use Geneziz/laya-1.7b with Ollama:
```
ollama run hf.co/Geneziz/laya-1.7b
```
Unsloth Desktop

How to use Geneziz/laya-1.7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Geneziz/laya-1.7b

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": "Geneziz/laya-1.7b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Geneziz/laya-1.7b with Docker Model Runner:
```
docker model run hf.co/Geneziz/laya-1.7b
```

Lemonade

How to use Geneziz/laya-1.7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Geneziz/laya-1.7b

Run and chat with the model

lemonade run user.laya-1.7b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Geneziz/laya-1.7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Geneziz/laya-1.7b

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 Geneziz/laya-1.7b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Geneziz/laya-1.7b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Geneziz/laya-1.7b

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 "Geneziz/laya-1.7b" \
  --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"

laya-1-7b-q4 (GGUF, Q4_K_M)

The on-device organizer model behind Geneziz's AI+ — the one-click enable that turns a personal web library into a local, searchable knowledge base. Geneziz downloads this model automatically, pins it by SHA-256, and runs it entirely on the user's machine.

Artifact


File	`laya-1-7b-q4.gguf`
Format	GGUF, Q4_K_M quantization
Size	1,107,408,576 bytes
SHA-256	`77deb49b431fcfb56def469c49b4aac14807007d54bbf3571af5218f601baba5`

Verify after downloading:

shasum -a 256 laya-1-7b-q4.gguf

It runs with llama.cpp and any compatible runtime.

Provenance and lineage

Trained by Geneziz as a parameter-efficient (LoRA) fine-tune on a private, Geneziz-collected dataset. The upstream base belongs to the Qwen3 family — upstream component licenses (Apache-2.0) remain honored and are credited here.

Intended use

Serving the Geneziz desktop app's local organizer: reading, classifying and organizing the user's own saved library, fully on the user's device. Out of scope: anything else.

License

Downloads last month: 363

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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