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--- |
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license: cc |
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tags: |
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- multimodal |
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- conversational |
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- GGUF |
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- Image-Text-to-Text |
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--- |
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# Omnivision |
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## Introduction |
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Omnivision is a compact, sub-billion (968M) multimodal model for processing both visual and text inputs, optimized for edge devices. Built on LLaVA's architecture, it features: |
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- **9x Token Reduction**: Reduces image tokens from 729 to 81, cutting latency and computational cost. |
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- **Minimal-Edit DPO**: Enhances response quality with minimal edits, preserving core model behavior. |
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**Quick Links:** |
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1. Interactive Demo in our [Hugging Face Space](https://huggingface.co/spaces/NexaAIDev/omnivlm-dpo-demo). |
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2. [Quickstart for local setup](#how-to-use-on-device) |
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3. Learn more in our [Blogs](https://nexa.ai) |
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**Feedback:** Send questions or comments about the model in our [Discord](https://discord.gg/nexa-ai) |
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## Intended Use Cases |
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Omnivision is designed for **Visual Question Answering** (answering questions about images) and **Image Captioning** (describing scenes in photos), making it ideal for on-device applications. |
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**Example Demo:** |
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Omni-Vision generated captions for a 1046×1568 pixel poster | **Processing time: <2s** | Device: MacBook M4 Pro |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/PTG3_n_p7_atBHCwRLOEE.png" alt="Example" style="width:700px;"/> |
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## Benchmarks |
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Below we demonstrate a figure to show how Omnivision performs against nanollava. In all the tasks, omnivision outperforms the previous world's smallest vision-language model. |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6618e0424dbef6bd3c72f89a/KsN-gTFM5MfJA5E3aDRJI.png" alt="Benchmark Radar Chart" style="width:500px;"/> |
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We have conducted a series of experiments on benchmark datasets, including MM-VET, ChartQA, MMMU, ScienceQA, POPE to evaluate the performance of omnivision. |
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| Benchmark | Nexa AI Omni-Vision | nanoLLAVA | Qwen2-VL-2B | |
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|-------------------|----------------------|-----------|-------------| |
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| MM-VET | 27.5 | 23.9 | 49.5 | |
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| ChartQA (Test) | 59.2 | NA | 73.5 | |
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| MMMU (Test) | 41.8 | 28.6 | 41.1 | |
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| MMMU (Eval) | 39.9 | 30.4 | 41.1 | |
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| ScienceQA (Eval) | 62.2 | 59.0 | NA | |
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| ScienceQA (Test) | 64.5 | 59.0 | NA | |
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| POPE | 89.4 | 84.1 | NA | |
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## How to Use On Device |
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In the following, we demonstrate how to run omnivision locally on your device. |
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**Step 1: Install Nexa-SDK (local on-device inference framework)** |
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[Install Nexa-SDK](https://github.com/NexaAI/nexa-sdk?tab=readme-ov-file#install-option-1-executable-installer) |
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> Nexa-SDK is a open-sourced, local on-device inference framework, supporting text generation, image generation, vision-language models (VLM), audio-language models, speech-to-text (ASR), and text-to-speech (TTS) capabilities. Installable via Python Package or Executable Installer. |
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**Step 2: Then run the following code in your terminal** |
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```bash |
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nexa run omnivision |
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``` |
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## Model Architecture ## |
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Omni-Vision's architecture consists of three key components: |
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- Base Language Model: Qwen2.5-0.5B-Instruct functions as the base model to process text inputs |
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- Vision Encoder: SigLIP-400M operates at 384 resolution with 14×14 patch size to generate image embeddings |
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- Projection Layer: Multi-Layer Perceptron (MLP) aligns the vision encoder's embeddings with the language model's token space |
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The vision encoder first transforms input images into embeddings, which are then processed by the projection layer to match the token space of Qwen2.5-0.5B-Instruct, enabling end-to-end visual-language understanding. |
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## Training |
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We developed Omni-Vision through a three-stage training pipeline: |
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**Pretraining:** |
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The initial stage focuses on establishing basic visual-linguistic alignments using image-caption pairs, during which only the projection layer parameters are unfrozen to learn these fundamental relationships. |
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**Supervised Fine-tuning (SFT):** |
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We enhance the model's contextual understanding using image-based question-answering datasets. This stage involves training on structured chat histories that incorporate images for the model to generate more contextually appropriate responses. |
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**Direct Preference Optimization (DPO):** |
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The final stage implements DPO by first generating responses to images using the base model. A teacher model then produces minimally edited corrections while maintaining high semantic similarity with the original responses, focusing specifically on accuracy-critical elements. These original and corrected outputs form chosen-rejected pairs. The fine-tuning targeted at essential model output improvements without altering the model's core response characteristics |
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## What's next? |
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We are continually improving Omnivision for better on-device performance. Stay tuned. |
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### Learn more in our blogs |
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[Blogs](https://nexa.ai) |
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### Join Discord Community |
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[Discord](https://discord.gg/nexa-ai) |
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