Instructions to use LaboAI/LaboAI-0.3.3-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use LaboAI/LaboAI-0.3.3-3B 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 LaboAI/LaboAI-0.3.3-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M

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 LaboAI/LaboAI-0.3.3-3B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M

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 LaboAI/LaboAI-0.3.3-3B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M

Use Docker

docker model run hf.co/LaboAI/LaboAI-0.3.3-3B:Q4_K_M

LM Studio
Jan
Ollama
How to use LaboAI/LaboAI-0.3.3-3B with Ollama:
```
ollama run hf.co/LaboAI/LaboAI-0.3.3-3B:Q4_K_M
```
Unsloth Desktop

How to use LaboAI/LaboAI-0.3.3-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M

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": "LaboAI/LaboAI-0.3.3-3B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LaboAI/LaboAI-0.3.3-3B with Docker Model Runner:
```
docker model run hf.co/LaboAI/LaboAI-0.3.3-3B:Q4_K_M
```

Lemonade

How to use LaboAI/LaboAI-0.3.3-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LaboAI/LaboAI-0.3.3-3B:Q4_K_M

Run and chat with the model

lemonade run user.LaboAI-0.3.3-3B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LaboAI/LaboAI-0.3.3-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M

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 LaboAI/LaboAI-0.3.3-3B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LaboAI/LaboAI-0.3.3-3B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf LaboAI/LaboAI-0.3.3-3B:Q4_K_M

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 "LaboAI/LaboAI-0.3.3-3B:Q4_K_M" \
  --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"

LaboAI-0.3.3-3B / README.md

Mmxa

Update README.md

1f56567 verified 1 day ago

preview code

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	---
	language:
	- en
	- es
	- code
	tags:
	- code-generation
	- android
	- kotlin
	- java
	- jetpack-compose
	- qwen2.5
	- unsloth
	- gguf
	- ollama
	- 3b
	license: apache-2.0
	base_model: Qwen/Qwen2.5-3B-Instruct
	datasets:
	- giggiovpg/ornith-android-instruct
	- giggiovpg/android-kotlin-compose-compiler-verified
	- microsoft/NextCoderDataset
	- glaiveai/glaive-code-assistant-v3
	---

	# 🤖 LaboAI-0.3.3-3B

	This is a lightweight yet capable language model (3B parameters) fine-tuned specifically for generating, understanding, and debugging Kotlin code and Android development (with a strong emphasis on Jetpack Compose, Coroutines, and modern architectures).

	This version (0.3.3) applies the same proven, multi-dataset training recipe as the 1.5B version, but scaled up to the 3B architecture for improved reasoning, better context understanding, and more reliable code generation. It is optimized using QLoRA (4-bit) to run efficiently on consumer hardware (e.g., NVIDIA RTX 3060 12GB, or RTX 4060).

	## 📋 Model Details

	- Developed by: Mmxa
	- Organization: LaboAI
	- Model type: Causal Language Model (Code Generation)
	- Languages: Kotlin, Java, English, Spanish (instructions)
	- License: Apache 2.0 (inherited from Qwen2.5)
	- Base model: [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)

	## 🚀 Uses

	### Direct Use
	- Generating robust boilerplate for Activities, Fragments, ViewModels, and Repositories in Kotlin.
	- Creating complex modern UI components with Jetpack Compose.
	- Debugging compilation errors or logic flaws in Android code snippets.
	- Translating legacy Java logic into modern, idiomatic Kotlin.

	### Ecosystem Use (Recommended)
	This model shines when used as a local coding assistant via Ollama and the Continue extension in VS Code. This guarantees complete privacy (your code never leaves your machine) and low latency.

	### Out-of-Scope Uses
	- It is not optimized for general chat, creative writing, or complex mathematical reasoning.
	- It should not be used to generate malicious code or exploits.
	- All generated code must be reviewed by a human developer before being merged into a main branch.

	## ⚠️ Limitations and Risks
	- API Hallucinations: In rare cases, it might suggest deprecated Android APIs instead of modern alternatives.
	- Context Window: Optimized for 1024 tokens during training. It is not suitable for analyzing massive, multi-thousand-line codebase files all at once.
	- Dependencies: It does not have real-time knowledge of the latest Android library updates.

	## 💻 How to Get Started (Local Setup)

	This repository includes both the original format (`safetensors`) and the quantized format (`GGUF` Q4_K_M).

	1. Install [Ollama](https://ollama.com/).
	2. Run the model directly from Hugging Face:
	```bash
	ollama run hf.co/LaboAI/LaboAI-0.3.3-3B:Q4_K_M