Overview
This lecture introduces the GLM-4.1V-9B-Thinking vision-language model, highlighting its reasoning capabilities, performance benchmarks, multilingual support, and basic usage instructions.
Model Introduction
- Vision-Language Models (VLMs) combine visual and language understanding for intelligent systems.
- Complex AI tasks require VLMs to not only perceive but also reason about multimodal data.
- GLM-4.1V-9B-Thinking is built on the GLM-4-9B-0414 foundation model.
- The model uses a "thinking paradigm" and reinforcement learning to enhance reasoning.
- It achieves state-of-the-art results among 10B-parameter models, rivaling much larger models like Qwen-2.5-VL-72B.
- Both the full model and base model (GLM-4.1V-9B-Base) are open-sourced for research.
- Improvements include: reasoning focus, 64k context length, support for arbitrary aspect ratios, up to 4K resolution, and Chinese-English bilingual capability.
Benchmark Performance
- Incorporating Chain-of-Thought reasoning improves answer accuracy, explanation richness, and interpretability.
- Outperforms traditional visual models without reasoning capabilities.
- Achieved top performance among 10B-level models on 23 out of 28 benchmark tasks.
- Outperformed 72B-parameter Qwen-2.5-VL-72B on 18 tasks.
Quick Inference
- To use the model, install the
transformers library from source.
- Example code demonstrates how to run single-image inference using Python and the Transformers API.
- Model can process both images and text in a single input, returning descriptive outputs.
- Additional capabilities include video reasoning and web demo deployment via the project's GitHub.
Key Terms & Definitions
- Vision-Language Model (VLM) — AI model that processes both images and text for understanding and reasoning tasks.
- Chain-of-Thought Reasoning — Technique where the model explains steps taken to reach an answer, improving transparency.
- Context Length — The amount of input (text or image) the model can process at once.
Action Items / Next Steps
- Try the GLM-4.1V-9B-Thinking model via Hugging Face, ModelScope, or the provided code sample.
- Explore further documentation and demos on the project's GitHub.
- Review the linked research paper for a deeper technical understanding.