Omega — Compact Fine-tuned Vision Model

Fine-tuned · 2B Parameters · 256K Context · Vision + Text · 1.9 GB

Omega is a small, fine-tuned multimodal model designed for fast local inference, prototyping, and training experiments. At under 2GB, it runs on CPU-only machines and low-RAM environments while still supporting image inputs and a 256K context window.

Quick Start

ollama run aeline/Omega

Requires Ollama 0.12.7 or later

Why Omega?

Most capable vision models are large — 7B, 14B, 70B. Omega fills the gap at 2B: small enough to run anywhere, capable enough to be genuinely useful for lightweight tasks, prototyping pipelines, and as a base for further fine-tuning.

It is also unrestricted, making it suitable as a training target — you can use Omega outputs as training data or as a reference model without hitting refusals.

Use Cases

Prototyping & Development Spin up a multimodal model instantly without needing a GPU. Test vision pipelines, prompt formats, and application logic before scaling up to a larger model.

Training & Fine-tuning Use Omega as a small base for fine-tuning experiments. At 2B parameters, training runs are fast and cheap. Good for testing dataset quality, training configs, and loss behavior before committing to a full-scale run.

Lightweight Vision Tasks Image description, basic OCR, simple visual Q&A — tasks where a 2B model is sufficient and speed matters more than depth.

Edge / CPU Deployment Runs on 6GB RAM with no GPU required. Suitable for edge devices, local assistants, or environments where GPU is unavailable.

System Requirements

Model Details

Limitations

• At 2B scale, complex multi-step reasoning and nuanced language tasks will underperform larger models
• Not recommended as a production model for high-stakes applications
• Best used as a fast iteration tool or training base

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Made by sk16er