Instructions to use fiel1986/andromeda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fiel1986/andromeda with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fiel1986/andromeda",
	filename="qwen2.5-3b-q4_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fiel1986/andromeda 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 fiel1986/andromeda:Q4_0
# Run inference directly in the terminal:
llama cli -hf fiel1986/andromeda:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fiel1986/andromeda:Q4_0
# Run inference directly in the terminal:
llama cli -hf fiel1986/andromeda:Q4_0

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 fiel1986/andromeda:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf fiel1986/andromeda:Q4_0

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 fiel1986/andromeda:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fiel1986/andromeda:Q4_0

Use Docker

docker model run hf.co/fiel1986/andromeda:Q4_0

LM Studio
Jan
Ollama
How to use fiel1986/andromeda with Ollama:
```
ollama run hf.co/fiel1986/andromeda:Q4_0
```

Unsloth Studio

How to use fiel1986/andromeda 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 fiel1986/andromeda 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 fiel1986/andromeda to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fiel1986/andromeda to start chatting

How to use fiel1986/andromeda with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fiel1986/andromeda:Q4_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "fiel1986/andromeda:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use fiel1986/andromeda with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fiel1986/andromeda:Q4_0

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 fiel1986/andromeda:Q4_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use fiel1986/andromeda with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fiel1986/andromeda:Q4_0

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 "fiel1986/andromeda:Q4_0" \
  --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"

Docker Model Runner
How to use fiel1986/andromeda with Docker Model Runner:
```
docker model run hf.co/fiel1986/andromeda:Q4_0
```

Lemonade

How to use fiel1986/andromeda with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fiel1986/andromeda:Q4_0

Run and chat with the model

lemonade run user.andromeda-Q4_0

List all available models

lemonade list

Qwen2.5-3B (Quantized 4-bit - Q4_0)

Este repositorio contiene el modelo Qwen2.5-3B cuantizado en formato GGUF con el esquema Q4_0. Este modelo es ideal para ejecutar inferencia localmente con baja latencia y un uso eficiente de la memoria RAM/VRAM, gracias a herramientas como llama.cpp, LM Studio, KoboldCPP o Ollama.

Detalles del Modelo

Modelo Base: Qwen2.5-3B (de Alibaba Cloud)
Formato: GGUF
Nivel de Cuantización: Q4_0 (4-bit)
Tamaño de archivo: Aprox. 1.8 GB - 2.0 GB (dependiendo del tokenizer)
Capacidades: Generación de texto, chat, razonamiento básico y seguimiento de instrucciones.
Licencia: Apache 2.0 (verifique la licencia específica del modelo base en el repositorio oficial de Qwen).

Cómo usar este modelo

Puedes cargar y ejecutar este modelo utilizando varias herramientas compatibles con GGUF.

1. Usando `llama.cpp`

Si tienes compilado llama.cpp, puedes ejecutar la inferencia directamente:

./main -m qwen2.5-3b-q4_0.gguf -p "Hola, ¿cómo estás?" -n 256

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GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Qwen2.5-3B (Quantized 4-bit - Q4_0)

Detalles del Modelo

Cómo usar este modelo

1. Usando llama.cpp

1. Usando `llama.cpp`