Instructions to use automajicly/qwen-1.5b-android with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use automajicly/qwen-1.5b-android with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="automajicly/qwen-1.5b-android", filename="qwen-1.5b-base.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 automajicly/qwen-1.5b-android 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 automajicly/qwen-1.5b-android # Run inference directly in the terminal: llama cli -hf automajicly/qwen-1.5b-android
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf automajicly/qwen-1.5b-android # Run inference directly in the terminal: llama cli -hf automajicly/qwen-1.5b-android
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 automajicly/qwen-1.5b-android # Run inference directly in the terminal: ./llama-cli -hf automajicly/qwen-1.5b-android
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 automajicly/qwen-1.5b-android # Run inference directly in the terminal: ./build/bin/llama-cli -hf automajicly/qwen-1.5b-android
Use Docker
docker model run hf.co/automajicly/qwen-1.5b-android
- LM Studio
- Jan
- Ollama
How to use automajicly/qwen-1.5b-android with Ollama:
ollama run hf.co/automajicly/qwen-1.5b-android
- Unsloth Studio
How to use automajicly/qwen-1.5b-android 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 automajicly/qwen-1.5b-android 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 automajicly/qwen-1.5b-android to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for automajicly/qwen-1.5b-android to start chatting
- Pi
How to use automajicly/qwen-1.5b-android with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf automajicly/qwen-1.5b-android
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": "automajicly/qwen-1.5b-android" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use automajicly/qwen-1.5b-android with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf automajicly/qwen-1.5b-android
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 automajicly/qwen-1.5b-android
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use automajicly/qwen-1.5b-android with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf automajicly/qwen-1.5b-android
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 "automajicly/qwen-1.5b-android" \ --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 automajicly/qwen-1.5b-android with Docker Model Runner:
docker model run hf.co/automajicly/qwen-1.5b-android
- Lemonade
How to use automajicly/qwen-1.5b-android with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull automajicly/qwen-1.5b-android
Run and chat with the model
lemonade run user.qwen-1.5b-android-{{QUANT_TAG}}List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
title: Qwen 1.5B Android emoji: π€ colorFrom: green colorTo: pink pinned: false license: mit language: - en tags: - quantized - gguf - android - mobile - uncensored - qwen - local-ai pipeline_tag: text-generation
π€ Qwen 2.5-1.5B Android
Quantized versions of Qwen 2.5-1.5B Instruct optimized for Android devices.
π¦ Models
- qwen-1.5b-q4.gguf (1.12 GB): High-end Android phones (8GB+ RAM)
- qwen-1.5b-q3.gguf (924 MB): Mid-range Android phones (4GB+ RAM)
Both use GGUF format for fast inference with llama.cpp.
π² Usage
- Download the model file
- Install a GGUF-compatible app (MNN LLM, ChatterUI, or similar)
- Load the model and run fully local β zero cloud dependency
π Links
- Downloads last month
- 74
We're not able to determine the quantization variants.