See our collection for all Qwen2.5-VL sizes.

Run Qwen2.5-VL with Keras 3: JAX, PyTorch, or TensorFlow

kerasformers/qwen2.5-vl-72b-instruct

Pure-Keras 3 conversion of Qwen/Qwen2.5-VL-72B-Instruct for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX. This is the 72B variant, the largest Qwen2.5-VL, served here as image + text -> text via Qwen2_5VLProcessor; weights are sharded and 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: YaRN: Efficient Context Window Extension of Large Language Models (arXiv:2309.00071) · HF Papers

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

✨ Quick start

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

from PIL import Image
from kerasformers.models.qwen2_5_vl import Qwen2_5VLConditionalGenerate, Qwen2_5VLProcessor

model = Qwen2_5VLConditionalGenerate.from_weights("kerasformers/qwen2.5-vl-72b-instruct")
processor = Qwen2_5VLProcessor.from_weights("kerasformers/qwen2.5-vl-72b-instruct")

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]))

Loading a 72B checkpoint

At 72B the checkpoint is sharded and does not fit comfortably on a single consumer GPU. These flags compose, and each trades a different resource:

model = Qwen2_5VLConditionalGenerate.from_weights(
    "kerasformers/qwen2.5-vl-72b-instruct",
    quantization="int8",     # weight-only int8 on Dense / Embedding (~4x smaller)
)

Load any Qwen2.5-VL variant the same way with from_weights("kerasformers/<variant>"):

Variant	Hub
`qwen2.5-vl-3b-instruct`	kerasformers/qwen2.5-vl-3b-instruct
`qwen2.5-vl-7b-instruct`	kerasformers/qwen2.5-vl-7b-instruct
`qwen2.5-vl-32b-instruct`	kerasformers/qwen2.5-vl-32b-instruct
`qwen2.5-vl-72b-instruct`	kerasformers/qwen2.5-vl-72b-instruct