Glyph

Glyph: Scaling Context Windows via Visual-Text Compression#

• Repository: https://github.com/thu-coai/Glyph
• Paper: https://arxiv.org/abs/2510.17800

Image: abs

Glyph is a framework for scaling the context length through visual-text compression. Instead of extending token-based context windows, Glyph renders long textual sequences into images and processes them using vision–language models (VLMs). This design transforms the challenge of long-context modeling into a multimodal problem, substantially reducing computational and memory costs while preserving semantic information.

Backbone Model#

Our model is built on GLM-4.1V-9B-Base.

Quick Inference#

This is a simple example of running single-image inference using the transformers library. First, install the transformers library:

pip install transformers>=4.57.1

Then, run the following code:

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "url": "https://raw.githubusercontent.com/thu-coai/Glyph/main/assets/Little_Red_Riding_Hood.png"
            },
            {
                "type": "text",
                "text": "Who pretended to be Little Red Riding Hood's grandmother"
            }
        ],
    }
]
processor = AutoProcessor.from_pretrained("zai-org/Glyph")
model = AutoModelForImageTextToText.from_pretrained(
    pretrained_model_name_or_path="zai-org/Glyph",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=8192)
output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)

See our Github Repo for more detailed usage.

Known Limitations#

• Sensitivity to rendering parameters: Glyph’s performance can vary with rendering settings such as resolution, font, and spacing. Since our search procedure adopts a fixed rendering configuration during post-training, the model may not generalize well to unseen or substantially different rendering styles.
• OCR-related challenges: Recognizing fine-grained or rare alphanumeric strings (e.g., UUIDs) remains difficult for visual-language models, especially with ultra-long inputs, sometimes leading to minor character misclassification.
• Limited generalization: The training of Glyph mainly targets long-context understanding, and its capability on broader tasks is yet to be studied.

Citation#

If you find our model useful in your work, please cite it with:

@article{cheng2025glyphscalingcontextwindows,
    title={Glyph: Scaling Context Windows via Visual-Text Compression}, 
    author={Jiale Cheng and Yusen Liu and Xinyu Zhang and Yulin Fei and Wenyi Hong and Ruiliang Lyu and Weihan Wang and Zhe Su and Xiaotao Gu and Xiao Liu and Yushi Bai and Jie Tang and Hongning Wang and Minlie Huang},
    journal={arXiv preprint arXiv:2510.17800},
    year={2025}
}