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kerasformers/
glm-4.6v-flash

Name: glm-4.6v-flash
Availability: InStock

zai-org/GLM-4.6V-Flash

finetune

mit

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Run GLM-4V with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

HuggingFace

zeromodels/glm-4.6v-flash

Pure-Keras 3 conversion of zai-org/GLM-4.6V-Flash for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX. GLM-4.6V-Flash is a vision-language model (GLM-4V vision tower + GLM-4 dense decoder) served as image + text -> text via Glm4vProcessor; weights are stored in bfloat16.

For model details, license, and usage terms, see the upstream model card.

Paper: GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning (arXiv:2507.01006) · HF Papers

✨ Quick start

python

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from PIL import Image
from zeromodels.models.glm4v import Glm4vConditionalGenerate, Glm4vProcessor

model = Glm4vConditionalGenerate.from_weights("zeromodels/glm-4.6v-flash")
processor = Glm4vProcessor.from_weights("zeromodels/glm-4.6v-flash")

inputs = processor(conversation=[
    {"role": "user", "content": [
        {"type": "image", "image": Image.open("photo.jpg")},
        {"type": "text", "text": "Describe this image in one sentence."},
    ]}
])
outputs = model.generate(**inputs, max_new_tokens=64)
print(processor.decode(outputs[0]))

Load any GLM variant the same way with from_weights("zeromodels/<variant>"). Browse them all in the GLM collection.

Special Thanks

A huge thank you to the Zhipu AI / THUDM team for creating and releasing the GLM models.

License: mit (per the upstream model card).

kerasformers/glm-4.6v-flash

Run GLM-4V with Keras 3: JAX, PyTorch, or TensorFlow

zeromodels/glm-4.6v-flash

✨ Quick start

Special Thanks

kerasformers/
glm-4.6v-flash