Instructions to use LaboAI/LaboAI-0.3.3-1.5B 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-0.3.3-1.5B 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-0.3.3-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.3.3-1.5B: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-0.3.3-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.3.3-1.5B: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-0.3.3-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LaboAI/LaboAI-0.3.3-1.5B: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-0.3.3-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LaboAI/LaboAI-0.3.3-1.5B:Q4_K_M

Use Docker

docker model run hf.co/LaboAI/LaboAI-0.3.3-1.5B:Q4_K_M

LM Studio
Jan
Ollama
How to use LaboAI/LaboAI-0.3.3-1.5B with Ollama:
```
ollama run hf.co/LaboAI/LaboAI-0.3.3-1.5B:Q4_K_M
```
Unsloth Desktop

How to use LaboAI/LaboAI-0.3.3-1.5B 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-0.3.3-1.5B: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-0.3.3-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LaboAI/LaboAI-0.3.3-1.5B with Docker Model Runner:
```
docker model run hf.co/LaboAI/LaboAI-0.3.3-1.5B:Q4_K_M
```

Lemonade

How to use LaboAI/LaboAI-0.3.3-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LaboAI/LaboAI-0.3.3-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.LaboAI-0.3.3-1.5B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LaboAI/LaboAI-0.3.3-1.5B 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-0.3.3-1.5B: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-0.3.3-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LaboAI/LaboAI-0.3.3-1.5B 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-0.3.3-1.5B: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-0.3.3-1.5B: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-0.3.3-1.5B / README.md

Mmxa

Update README.md

7d29255 verified 1 day ago

preview code

Raw History Blame Contribute Delete

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	---
	language:
	- en
	- es
	- code
	tags:
	- code-generation
	- android
	- kotlin
	- java
	- jetpack-compose
	- qwen2.5
	- unsloth
	- gguf
	- ollama
	- 1.5b
	license: apache-2.0
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	datasets:
	- giggiovpg/ornith-android-instruct
	- giggiovpg/android-kotlin-compose-compiler-verified
	- microsoft/NextCoderDataset
	- glaiveai/glaive-code-assistant-v3
	---

	# LaboAI-0.3.3-1.5B

	This is a lightweight language model (1.5B 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) is the result of an optimized, multi-dataset training regimen designed for extreme efficiency. It is optimized using QLoRA (4-bit) to run locally on GPUs with limited VRAM (such as the NVIDIA Quadro M2000 with 4GB) without sacrificing code quality.

	## 📋 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/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)

	## 🚀 Uses

	### Direct Use
	- Generating boilerplate for Activities, Fragments, ViewModels, and Repositories in Kotlin.
	- Creating 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 ultra-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 1024-2048 tokens. 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). To use it on your PC with a 4GB VRAM GPU:

	1. Install [Ollama](https://ollama.com/).
	2. Download the `.gguf` file from this repository (e.g., `LaboAI-0.3.3-1.5B-Q4_K_M.gguf`).
	3. Create a file named `Modelfile` in the same folder with the following content:
	```text
	FROM ./LaboAI-0.3.3-1.5B-Q4_K_M.gguf
	PARAMETER stop "### Instruction:"
	PARAMETER stop "### Response:"