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

🚀 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.
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:

   FROM ./LaboAI-0.3.3-1.5B-Q4_K_M.gguf
   PARAMETER stop "### Instruction:"
   PARAMETER stop "### Response:"