transformers/docs/source/en/model_doc/glm4v.md

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# GLM-4.1V

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

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline
pipe = pipeline(
    task="image-text-to-text",
    model="THUDM/GLM-4.1V-9B-Thinking",
    device=0,
    torch_dtype=torch.bfloat16
)
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)
```
</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import Glm4vForConditionalGeneration, AutoProcessor

model = Glm4vForConditionalGeneration.from_pretrained(
    "THUDM/GLM-4.1V-9B-Thinking",
    torch_dtype=torch.bfloat16,
    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("cuda")

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)
```
</hfoption>
</hfoptions>

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.

```python
from transformers import AutoProcessor, Glm4vForConditionalGeneration
import torch

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",
    torch_dtype=torch.bfloat16,
    device_map="cuda:0"
)

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("cuda:0")
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

## Glm4vTextConfig

[[autodoc]] Glm4vTextConfig

## Glm4vImageProcessor

[[autodoc]] Glm4vImageProcessor
    - preprocess

## Glm4vVideoProcessor

[[autodoc]] Glm4vVideoProcessor
    - preprocess

## Glm4vImageProcessorFast

[[autodoc]] Glm4vImageProcessorFast
    - preprocess

## Glm4vProcessor

[[autodoc]] Glm4vProcessor

## Glm4vTextModel

[[autodoc]] Glm4vTextModel
    - forward

## Glm4vModel

[[autodoc]] Glm4vModel
    - forward

## Glm4vForConditionalGeneration

[[autodoc]] Glm4vForConditionalGeneration
    - forward