This model was released on 2025-07-01 and added to Hugging Face Transformers on 2025-06-25.

GLM-V

Overview

The GLM-V model was proposed in GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

The abstract from the paper is the following:

We present GLM-4.1V-Thinking, GLM-4.5V, and GLM-4.6V, a family of vision-language models (VLMs) designed to advance general-purpose multimodal understanding and reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework. We first develop a capable vision foundation model with significant potential through large-scale pre-training, which arguably sets the upper bound for the final performance. We then propose Reinforcement Learning with Curriculum Sampling (RLCS) to unlock the full potential of the model, leading to comprehensive capability enhancement across a diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding, GUI-based agents, and long document interpretation. In a comprehensive evaluation across 42 public benchmarks, GLM-4.5V achieves state-of-the-art performance on nearly all tasks among open-source models of similar size, and demonstrates competitive or even superior results compared to closed-source models such as Gemini-2.5-Flash on challenging tasks including Coding and GUI Agents. Meanwhile, the smaller GLM-4.1V-9B-Thinking remains highly competitive-achieving superior results to the much larger Qwen2.5-VL-72B on 29 benchmarks. We open-source both GLM-4.1V-9B-Thinking and GLM-4.5V. We further introduce the GLM-4.6V series, open-source multimodal models with native tool use and a 128K context window. A brief overview is available at this https URL. Code, models and more information are released at https://github.com/zai-org/GLM-V

Support Model

This Model type support these model of zai-org:

• GLM-4.1V-9B-Base
• GLM-4.1V-9B-Thinking
• GLM-4.6V-Flash
• AutoGLM-Phone-9B
• AutoGLM-Phone-9B-Multilingual
• Glyph
• WebVIA-Agent
• UI2Code_N

This model was contributed by Raushan Turganbay and Yuxuan Zhang.

Usage

The example below demonstrates how to generate text based on an image with [Pipeline] or the [AutoModel] class.

from transformers import pipeline


pipe = pipeline(
    task="image-text-to-text",
    model="THUDM/GLM-4.1V-9B-Thinking",
    device=0,
)
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ]
    }
]
pipe(text=messages, max_new_tokens=20, return_full_text=False)

from transformers import AutoProcessor, Glm4vForConditionalGeneration


model = Glm4vForConditionalGeneration.from_pretrained(
    "THUDM/GLM-4.1V-9B-Thinking",
    device_map="auto",
    attn_implementation="sdpa"
)
processor = AutoProcessor.from_pretrained("THUDM/GLM-4.1V-9B-Thinking")
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
            },
            {
                "type": "text",
                "text": "Describe this image."
            }
        ]
    }

]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Using GLM-4.1V with video input is similar to using it with image input. The model can process video data and generate text based on the content of the video.

from transformers import AutoProcessor, Glm4vForConditionalGeneration


processor = AutoProcessor.from_pretrained("THUDM/GLM-4.1V-9B-Thinking")
model = Glm4vForConditionalGeneration.from_pretrained(
    pretrained_model_name_or_path="THUDM/GLM-4.1V-9B-Thinking",
    device_map="auto"
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "url": "https://test-videos.co.uk/vids/bigbuckbunny/mp4/h264/720/Big_Buck_Bunny_720_10s_10MB.mp4",
            },
            {
                "type": "text",
                "text": "discribe this video",
            },
        ],
    }
]
inputs = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=True,
                                       return_tensors="pt", padding=True).to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=1.0)
output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(output_text)

Glm4vConfig

[[autodoc]] Glm4vConfig

Glm4vVisionConfig

[[autodoc]] Glm4vVisionConfig

Glm4vTextConfig

[[autodoc]] Glm4vTextConfig

Glm4vImageProcessor

[[autodoc]] Glm4vImageProcessor - preprocess

Glm4vVideoProcessor

[[autodoc]] Glm4vVideoProcessor

• preprocess

Glm4vImageProcessorPil

[[autodoc]] Glm4vImageProcessorPil - preprocess

Glm4vProcessor

[[autodoc]] Glm4vProcessor - call

Glm4vVisionModel

[[autodoc]] Glm4vVisionModel - forward

Glm4vTextModel

[[autodoc]] Glm4vTextModel - forward

Glm4vModel

[[autodoc]] Glm4vModel - forward - get_video_features - get_image_features

Glm4vForConditionalGeneration

[[autodoc]] Glm4vForConditionalGeneration - forward - get_video_features - get_image_features