pipeline_tag: image-text-to-text
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
base_model: OpenGVLab/InternVL3-1B-hf
library_name: kerasformers
language:
- en
tags:
- keras
- kerasformers
- internvl
- internvl3
- multimodal
- vision
- image-text-to-text
- pytorch
- jax
- tf
license_name: qwen
Run InternVL3 with Keras 3: JAX, PyTorch, or TensorFlow
kerasformers/internvl3-1b
Pure-Keras 3 conversion of OpenGVLab/InternVL3-1B-hf for kerasformers. One implementation runs unmodified on TensorFlow / Torch / JAX. This is a InternVL3 checkpoint, served as image + text -> text via InternVLProcessor; weights are stored in bfloat16.
For model details, license, and usage terms, see the upstream model card.
Paper: InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models (arXiv:2504.10479) · HF Papers
Paper: Qwen2.5 Technical Report (arXiv:2412.15115) · HF Papers
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from kerasformers.models.internvl import InternVLConditionalGenerate, InternVLProcessor
model = InternVLConditionalGenerate.from_weights("kerasformers/internvl3-1b")
processor = InternVLProcessor.from_weights("kerasformers/internvl3-1b")
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 InternVL variant the same way with from_weights("kerasformers/<variant>"). Browse them all in the InternVL collection.
Special Thanks
A huge thank you to the OpenGVLab team for creating and releasing the InternVL models.
License: see the upstream license.