🤖 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

🚀 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.
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:

   FROM ./LaboAI-0.3.2-1.5B-Q4_K_M.gguf
   PARAMETER stop "### Instruction:"
   PARAMETER stop "### Response:"