ORBIX – Enhancing OpenAI’s GPT-OSS with Multimodal Vision Capabilities extensible to ISRO EO Data
Here's a detailed description you can use for uploading your "Orbix: Augmenting GPT-OSS" diagrams to YouTube, tailored to attract relevant viewers and provide good context:
Video Title Idea:
Orbix: Augmenting GPT-OSS - Multimodal Architecture & Workflow for Vision-Enabled LLMs (Open-Source)
YouTube Description:
Orbix: Augmenting GPT-OSS - Multimodal Architecture & Workflow
This video presents the "Orbix" project, which outlines a technical approach to augment OpenAI's powerful GPT-OSS (20B / 120B) language models with visual perception capabilities. While GPT-OSS excels in text-based reasoning, Orbix aims to bridge the gap by integrating vision understanding, enabling advanced multimodal applications like image captioning, visual question answering (VQA), and multimodal instruction-following.
What you'll find in this video:
Orbix Architecture Diagram: A clear, comprehensive visual breakdown of the proposed system. This diagram illustrates the key components, including the data layer, vision encoder (e.g., OpenCLIP ViT), the lightweight projection & alignment module (Q-former style) that connects vision embeddings to GPT-OSS, and the training & optimization frameworks used. It also highlights the application and EO integration, demonstrating how Orbix can be used for Earth Observation data analysis.
Orbix Workflow Diagram: A step-by-step visual guide detailing the entire process from multimodal data ingestion and preprocessing, through model training and fine-tuning with techniques like PEFT/LoRA, to deployment and application for generating natural language outputs and performing EO data analysis.
Key Challenges Orbix Addresses:
Aligning vision embeddings with LLM's text embedding space without degrading textual capabilities.
Sourcing high-quality, open, and permissive multimodal datasets.
Achieving competitive multimodal performance within reasonable compute budgets.
Expected Outcomes & Impact:
Orbix provides a reproducible framework for creating a multimodal GPT-OSS model trained with publicly available datasets and efficient strategies. This project is a foundation for extending GPT-OSS into more advanced multimodal reasoning domains, with significant potential for:
ISRO EO Data Linkage: Highly accurate & automated Land-cover classification, change detection, and environmental monitoring from satellite imagery.
Conversational Geospatial Analytics: Enabling conversational exploration of large geospatial archives, interactive Q&A over time-series imagery, and generation of rich, human-readable reports that combine spatial analytics with domain-specific reasoning.
Technical Stack Highlights:
Language Model: GPT-OSS (20B / 120B) via Hugging Face Transformers, PEFT / LoRA.
Vision Encoder: OpenCLIP ViT / Swin Transformer.
Data: COCO Captions, VQAv2, Visual Genome, Conceptual Captions, LAION-5B, BigEarthNet, EuroSAT, Sentinel-2 / Landsat.
Frameworks: PyTorch, PyTorch Lightning / Hugging Face Accelerate, DeepSpeed / FSDP.
EO Tools: Rasterio, Geopandas, SentinelHub-Py, Google Earth Engine Python API.
Deployment: vLLM, FastAPI, Gradio / Streamlit.
This project represents a significant step towards creating powerful, open-source multimodal AI systems capable of understanding and interacting with both text and visual information, particularly in specialized domains like Earth Observation.
#LLM #GPTOSS #MultimodalAI #ComputerVision #NaturalLanguageProcessing #DeepLearning #OpenSourceAI #EarthObservation #SatelliteImagery #AIArchitecture #Workflow #MachineLearning #AITraining #HuggingFace #PyTorch #DataScience
Here’s a detailed description you can use for uploading your “Orbix: Augmenting GPT-OSS” diagrams to YouTube, tailored to attract relevant viewers and provide good context:
Video Title Idea:
Orbix: Augmenting GPT-OSS – Multimodal Architecture & Workflow for Vision-Enabled LLMs (Open-Source)
YouTube Description:
Orbix: Augmenting GPT-OSS – Multimodal Architecture & Workflow
This video presents the “Orbix” project, which outlines a technical approach to augment OpenAI’s powerful GPT-OSS (20B / 120B) language models with visual perception capabilities. While GPT-OSS excels in text-based reasoning, Orbix aims to bridge the gap by integrating vision understanding, enabling advanced multimodal applications like image captioning, visual question answering (VQA), and multimodal instruction-following.
What you’ll find in this video:
Orbix Architecture Diagram: A clear, comprehensive visual breakdown of the proposed system. This diagram illustrates the key components, including the data layer, vision encoder (e.g., OpenCLIP ViT), the lightweight projection & alignment module (Q-former style) that connects vision embeddings to GPT-OSS, and the training & optimization frameworks used. It also highlights the application and EO integration, demonstrating how Orbix can be used for Earth Observation data analysis.
Orbix Workflow Diagram: A step-by-step visual guide detailing the entire process from multimodal data ingestion and preprocessing, through model training and fine-tuning with techniques like PEFT/LoRA, to deployment and application for generating natural language outputs and performing EO data analysis.
Key Challenges Orbix Addresses:
Aligning vision embeddings with LLM’s text embedding space without degrading textual capabilities.
Sourcing high-quality, open, and permissive multimodal datasets.
Achieving competitive multimodal performance within reasonable compute budgets.
Expected Outcomes & Impact:
Orbix provides a reproducible framework for creating a multimodal GPT-OSS model trained with publicly available datasets and efficient strategies. This project is a foundation for extending GPT-OSS into more advanced multimodal reasoning domains, with significant potential for:
ISRO EO Data Linkage: Highly accurate & automated Land-cover classification, change detection, and environmental monitoring from satellite imagery.
Conversational Geospatial Analytics: Enabling conversational exploration of large geospatial archives, interactive Q&A over time-series imagery, and generation of rich, human-readable reports that combine spatial analytics with domain-specific reasoning.
Technical Stack Highlights:
Language Model: GPT-OSS (20B / 120B) via Hugging Face Transformers, PEFT / LoRA.
Vision Encoder: OpenCLIP ViT / Swin Transformer.
Data: COCO Captions, VQAv2, Visual Genome, Conceptual Captions, LAION-5B, BigEarthNet, EuroSAT, Sentinel-2 / Landsat.
Frameworks: PyTorch, PyTorch Lightning / Hugging Face Accelerate, DeepSpeed / FSDP.
EO Tools: Rasterio, Geopandas, SentinelHub-Py, Google Earth Engine Python API.
Deployment: vLLM, FastAPI, Gradio / Streamlit.
This project represents a significant step towards creating powerful, open-source multimodal AI systems capable of understanding and interacting with both text and visual information, particularly in specialized domains like Earth Observation.
#LLM #GPTOSS #MultimodalAI #ComputerVision #NaturalLanguageProcessing #DeepLearning #OpenSourceAI #EarthObservation #SatelliteImagery #AIArchitecture #Workflow #MachineLearning #AITraining #HuggingFace #PyTorch #DataScience
