A beginner's guide to the Glm-4v-9b model by Cuuupid on Replicate

Source: Dev.to

Overview

Glm-4v-9b is a powerful multimodal language model developed by Tsinghua University. It demonstrates state‑of‑the‑art performance on several benchmarks, including optical character recognition (OCR). The model belongs to the GLM‑4 series, which also includes the base glm-4-9b model and the chat‑oriented variants glm-4-9b-chat and glm-4-9b-chat-1m.

Model Variants

• glm-4-9b – the base language model.
• glm-4-9b-chat – optimized for conversational use.
• glm-4-9b-chat-1m – a lightweight chat‑oriented version.
• glm-4v-9b – adds visual understanding capabilities to the series, enabling image‑related tasks.

Capabilities

The glm-4v-9b model can:

• Generate detailed image descriptions.
• Answer visual questions (VQA).
• Perform multimodal reasoning that combines text and visual information.
• Operate in both Chinese and English.

Comparison with Other Models

Compared to similar multimodal models such as sdxl-lightning-4step and cogvlm, glm-4v-9b stands out for its strong performance across a wide range of benchmarks. It has been shown to outperform models like GPT‑4, Gemini 1.0 Pro, and Claude 3 Opus on tasks involving both language and vision.

Using the Model

Input

• Image – any image you wish the model to process (e.g., a photograph, diagram, or scanned document).
• Prompt – a text description of the task or query, such as “Describe the scene in the image” or “What is the text shown in the picture?”

Output

The model returns a textual response that may include:

• A description of the input image.
• An answer to a visual question.
• Results of multimodal reasoning, combining visual and textual information.