Running large language models locally often demands expensive hardware with high VRAM. However, for specialized tasks like offline Q&A and knowledge retrieval, a lightweight, highly optimized small language model (SLM) can deliver incredible speed and efficiency without breaking the bank.
In this post, I will share how I fine-tuned moeinGTS 1.5B, quantized it to GGUF, and deployed it for fast offline inference.
🚀 Model Overview: moeinGTS 1.5B
moeinGTS is a compact 1.5B parameter language model fine-tuned specifically for fast, accurate question-and-answering based on Wikipedia and structured datasets.
Key Highlights:
Parameter Size: 1.5 Billion
Quantization: Q4_K_M & F16 GGUF
Download Size: ~1 GB
VRAM Requirements: Only ~1.5 GB
Primary Use Case: Offline local Q&A, low-latency edge AI applications
🛠️ The Pipeline: Fine-Tuning & Deployment
Dataset & Training: Fine-tuned on high-quality Q&A and knowledge retrieval datasets to improve concise factual responses.
LoRA Fine-Tuning: Applied Low-Rank Adaptation (LoRA) via PEFT to optimize training efficiency and keep resource usage minimal.
Quantization: Converted the model weights to GGUF format for seamless cross-platform execution on low-end hardware, CPUs, and edge devices.
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