Instructions to use LaboAI/LaboAI-0.3.2-1.5B 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-0.3.2-1.5B 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-0.3.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.3.2-1.5B: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-0.3.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LaboAI/LaboAI-0.3.2-1.5B: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-0.3.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LaboAI/LaboAI-0.3.2-1.5B: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-0.3.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LaboAI/LaboAI-0.3.2-1.5B:Q4_K_M

Use Docker

docker model run hf.co/LaboAI/LaboAI-0.3.2-1.5B:Q4_K_M

LM Studio
Jan
Ollama
How to use LaboAI/LaboAI-0.3.2-1.5B with Ollama:
```
ollama run hf.co/LaboAI/LaboAI-0.3.2-1.5B:Q4_K_M
```
Unsloth Desktop

How to use LaboAI/LaboAI-0.3.2-1.5B 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-0.3.2-1.5B: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-0.3.2-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use LaboAI/LaboAI-0.3.2-1.5B with Docker Model Runner:
```
docker model run hf.co/LaboAI/LaboAI-0.3.2-1.5B:Q4_K_M
```

Lemonade

How to use LaboAI/LaboAI-0.3.2-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LaboAI/LaboAI-0.3.2-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.LaboAI-0.3.2-1.5B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use LaboAI/LaboAI-0.3.2-1.5B 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-0.3.2-1.5B: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-0.3.2-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use LaboAI/LaboAI-0.3.2-1.5B 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-0.3.2-1.5B: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-0.3.2-1.5B: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"

Mmxa commited on 8 days ago

Commit

30f0475

verified ·

1 Parent(s): 612c3c8

Update README.md

Browse files

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README.md +61 -14

README.md CHANGED Viewed

@@ -1,23 +1,70 @@
 ---
 tags:
-- gguf
-- llama.cpp
 - unsloth
 ---
-# LaboAI-0.3.2-1.5B : 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-0.3.2-1.5B --jinja`
-- For multimodal models: `llama-mtmd-cli -hf LaboAI/LaboAI-0.3.2-1.5B --jinja`
-## Available Model files:
-- `Qwen2.5-1.5B-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
+- 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
 ---
+# 🤖 LaboAI-0.3.2-1.5B
+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 and modern architectures).
+It has been optimized using **QLoRA (4-bit)** to be extremely memory-efficient, allowing it to run locally on GPUs with limited VRAM (such as the NVIDIA Quadro M2000 with 4GB) without sacrificing response 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, or ViewModels 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 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 (e.g., `AsyncTask` or old XML layouts) instead of Coroutines or Compose.
+- **Context Window:** Limited to 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 (e.g., recent changes in Hilt or Room).
+## 💻 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-0.3.2-1.5B-Q4_K_M.gguf`).
+3. Create a file named `Modelfile` in the same folder with the following content:
+   ```text
+   FROM ./LaboAI-0.3.2-1.5B-Q4_K_M.gguf
+   PARAMETER stop "### Instruction:"
+   PARAMETER stop "### Response:"