kerasformers/qwen2-vl-72b

Run Qwen2-VL with Keras 3: JAX, PyTorch, or TensorFlow

kerasformers/qwen2-vl-72b

Pure-Keras 3 conversion of Qwen/Qwen2-VL-72B for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX. This is the 72B variant, served here as image + text -> text via Qwen2VLProcessor; weights are stored in bfloat16.

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

Paper: Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution (arXiv:2409.12191) · HF Papers

Paper: Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities (arXiv:2308.12966) · HF Papers

✨ Quick start

Text-only

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

from kerasformers.models.qwen2_vl import Qwen2VLTextGenerate, Qwen2VLProcessor

model = Qwen2VLTextGenerate.from_weights("kerasformers/qwen2-vl-72b")
processor = Qwen2VLProcessor.from_weights("kerasformers/qwen2-vl-72b")

inputs = processor(conversation=[
    {"role": "user", "content": [{"type": "text", "text": "Hello, who are you?"}]}
])
outputs = model.generate(**inputs, max_new_tokens=64)
print(processor.decode(outputs[0]))

Image + text

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

from PIL import Image
from kerasformers.models.qwen2_vl import Qwen2VLConditionalGenerate, Qwen2VLProcessor

model = Qwen2VLConditionalGenerate.from_weights("kerasformers/qwen2-vl-72b")
processor = Qwen2VLProcessor.from_weights("kerasformers/qwen2-vl-72b")

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 Qwen2-VL variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
qwen2-vl-72b	kerasformers/qwen2-vl-72b
qwen2-vl-72b-instruct	kerasformers/qwen2-vl-72b-instruct

Special Thanks

A huge thank you to the Qwen team at Alibaba for creating and releasing these models.

License: Qwen license (see the upstream license).