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"

Mmxa commited on 2 days ago

Commit

8ff68fb

verified ·

1 Parent(s): 39b3d48

Update README.md

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README.md +59 -14

README.md CHANGED Viewed

@@ -1,23 +1,68 @@
 ---
 tags:
-- gguf
-- llama.cpp
 - unsloth
 ---
-# LaboAI-0.3.3-3B : GGUF
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf LaboAI/LaboAI-0.3.3-3B --jinja`
-- For multimodal models: `llama-mtmd-cli -hf LaboAI/LaboAI-0.3.3-3B --jinja`
-## Available Model files:
-- `Qwen2.5-3B-Instruct.Q4_K_M.gguf`
-## Ollama
-An Ollama Modelfile is included for easy deployment.
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+language:
+- en
+- es
+- code
 tags:
+- code-generation
+- android
+- kotlin
+- java
+- jetpack-compose
+- qwen2.5
 - unsloth
+- gguf
+- ollama
+- 3b
+- function-calling
+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 versatile, 3-billion parameter language model heavily fine-tuned for **Kotlin** and **Android** development, while retaining strong general-purpose capabilities.
+Built on the robust `Qwen2.5-3B-Instruct` architecture, this model underwent a massive, high-quality fine-tuning regimen (v0.3.3). It excels at generating, understanding, and debugging modern Android code (Jetpack Compose, Coroutines, MVVM) but remains highly capable in general chat, reasoning, and tool-calling tasks.
+## 📋 Model Details
+- **Developed by:** Mmxa
+- **Organization:** LaboAI
+- **Model type:** Causal Language Model (Code Generation & General Assistant)
+- **Languages:** Kotlin, Java, English, Spanish
+- **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
+- **Android Development:** Generating boilerplate, Jetpack Compose UIs, ViewModels, and debugging Kotlin code.
+- **General Assistant:** Answering questions, summarizing text, and logical reasoning.
+- **Tool Calling:** Capable of structured JSON output for function calling and agentic workflows.
+### Ecosystem Use (Recommended)
+This model is optimized for local inference via **Ollama** and integrates seamlessly with the **Continue** extension in VS Code. It strikes the perfect balance between intelligence and local hardware efficiency.
+### Out-of-Scope Uses
+- It should not be used to generate malicious code, exploits, or harmful content.
+- All generated code must be reviewed by a human developer before deployment.
+## ⚠️ Limitations and Risks
+- **Context Window:** Optimized for 2048-4096 tokens. It may lose coherence in extremely long, multi-file contexts.
+- **API Hallucinations:** In rare cases, it might suggest slightly deprecated Android APIs.
+- **General Knowledge:** While fine-tuned for code, its general world knowledge is bounded by its base model and the fine-tuning data distribution.
+##  How to Get Started (Local Setup)
+This repository includes both the original format (`safetensors`) and the quantized format (`GGUF` Q4_K_M).
+### Quick Start with Ollama
+The fastest way to run this model is directly from Hugging Face via Ollama:
+```bash
+ollama run hf.co/LaboAI/LaboAI-0.3.3-3B:Q4_K_M