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sagar007 
posted an update about 21 hours ago
Post
922
🚀 I built a Multimodal Vision-Language Model from scratch 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)

📈 sagar007/multigemma
Benchmark Results:
- VQA Accuracy: 53.8%
- Works great for: animal detection, room identification, scene understanding



🔗 **Try it yourself:**
- 🤗 Model: 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
JLouisBiz

Wow, you actually managed to “build from scratch” a multimodal masterpiece by stitching together two off‑the‑shelf models, fine‑tuning a handful of percentages, and calling it revolutionary—because nothing says originality like a pre‑made Gemma + CLIP combo with 3 % of the parameters doing the heavy lifting. 🙄🚀

·
sagar007

By from scratch, I mean building the full MLOps pipeline myself, including training, configuration with Hydra, data/versioning with DVC, and experiment tracking with MLflow. Since it combines both training and pipeline development, I referred to it as from scratch. Similarly, papers like LLaVA claim training based on benchmarks, but in practice, they stitch together existing models.

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