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sagar007
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LLM AND STABLE DIFFUSION

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replied to their post about 10 hours ago
🚀 I built a Multimodal Vision-Language Model from using Gemma-270M + CLIP! Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results! 🔧 What I Built: A vision-language model that can understand images and answer questions about them, combining: - Google Gemma-3-270M (language) - OpenAI CLIP ViT-Large/14 (vision) - LoRA fine-tuning for efficiency 📊 Training Stats: - 157,712 training samples (full LLaVA dataset) - 3 epochs on A100 40GB - ~9 hours training time - Final loss: 1.333 training / 1.430 validation - Only 18.6M trainable params (3.4% of 539M total) 📈 https://huggingface.co/sagar007/multigemma Benchmark Results: - VQA Accuracy: 53.8% - Works great for: animal detection, room identification, scene understanding 🔗 **Try it yourself:** - 🤗 Model: https://huggingface.co/sagar007/multigemma - 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma - 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD! Would love to hear your feedback! 🙏 #multimodal #gemma #clip #llava #vision-language #pytorch
replied to their post 1 day ago
🚀 I built a Multimodal Vision-Language Model from using Gemma-270M + CLIP! Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results! 🔧 What I Built: A vision-language model that can understand images and answer questions about them, combining: - Google Gemma-3-270M (language) - OpenAI CLIP ViT-Large/14 (vision) - LoRA fine-tuning for efficiency 📊 Training Stats: - 157,712 training samples (full LLaVA dataset) - 3 epochs on A100 40GB - ~9 hours training time - Final loss: 1.333 training / 1.430 validation - Only 18.6M trainable params (3.4% of 539M total) 📈 https://huggingface.co/sagar007/multigemma Benchmark Results: - VQA Accuracy: 53.8% - Works great for: animal detection, room identification, scene understanding 🔗 **Try it yourself:** - 🤗 Model: https://huggingface.co/sagar007/multigemma - 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma - 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD! Would love to hear your feedback! 🙏 #multimodal #gemma #clip #llava #vision-language #pytorch
posted an update 1 day ago
🚀 I built a Multimodal Vision-Language Model from using Gemma-270M + CLIP! Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results! 🔧 What I Built: A vision-language model that can understand images and answer questions about them, combining: - Google Gemma-3-270M (language) - OpenAI CLIP ViT-Large/14 (vision) - LoRA fine-tuning for efficiency 📊 Training Stats: - 157,712 training samples (full LLaVA dataset) - 3 epochs on A100 40GB - ~9 hours training time - Final loss: 1.333 training / 1.430 validation - Only 18.6M trainable params (3.4% of 539M total) 📈 https://huggingface.co/sagar007/multigemma Benchmark Results: - VQA Accuracy: 53.8% - Works great for: animal detection, room identification, scene understanding 🔗 **Try it yourself:** - 🤗 Model: https://huggingface.co/sagar007/multigemma - 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma - 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD! Would love to hear your feedback! 🙏 #multimodal #gemma #clip #llava #vision-language #pytorch
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