Top Vision Models 2025: Qwen 2.5 VL, Moondream, & SmolVLM (Fine-Tuning & Benchmarks) скачать в хорошем качестве

Top Vision Models 2025: Qwen 2.5 VL, Moondream, & SmolVLM (Fine-Tuning & Benchmarks)

🔑 Get access to the ADVANCED-vision Repo: https://trelis.com/ADVANCED-vision/ 🛠 Explore Fine-tuning, Vision, Audio, and Inference Tools: https://Trelis.com 💡 Consulting (Technical Assistance OR Market Insights): https://forms.gle/2VXzrBzpvm1BmG6e7 🤝 Work for Trelis: https://trelis.com/jobs/ 💸 Grants Program: https://trelis.com/trelis-ai-grants/ 📧 Newsletter: https://trelis.substack.com 📸 Thumbnail made with this tutorial:    • Fine Tune Flux Diffusion Models with Your ...   VIDEO LINKS: Florence 2 Video (best quality small VLM):    • How to Fine-tune Florence 2: The Best Smal...   Data Preparation / Llava Video:    • Fine-tune Multi-modal LLaVA Vision and Lan...   SmolVLM Blog: https://huggingface.co/blog/smolervlm SmolVLM WebGPU demo: https://huggingface.co/spaces/Hugging... Moondream Blog: https://moondream.ai/blog/introducing... Qwen2.5-VL Blog: https://qwenlm.github.io/blog/qwen2.5... One-click-llms: https://github.com/TrelisResearch/one... TIMESTAMPS: 00:00 Introduction to Vision Language Models 00:55 Model Recommendations: Small vs Large 02:02 Exploring Moondream's Latest Features 03:00 Inference with Moondream 12:20 Fine-Tuning SmolVLM 12:55 Understanding SmolVLM Architecture 17:22 Fine-Tuning SmolVLM: Step-by-Step 32:54 Introducing Qwen 2.5 VL 37:48 Troubleshooting FlashAttention Installation 38:42 Updating Transformers and Restarting Kernel 39:50 Handling Token Limits and VRAM Issues 40:44 Evaluating Model Performance on Chess Pieces 42:48 Comparing Performance with Florence 2 44:46 Training Loop and Data Collator Setup 50:34 Addressing Memory Issues and Image Resolution 55:39 Final Training and Evaluation 01:04:22 Inference and Model Comparison 01:08:27 Conclusion and WebGPU Demo