pipeline_tag: image-text-to-text
license: apache-2.0
base_model: OpenGVLab/InternVL3_5-14B-HF
library_name: zeromodels
language:
- en
tags:
- keras
- zeromodels
- internvl
- internvl3-5
- multimodal
- vision
- image-text-to-text
- pytorch
- jax
- tf
Run InternVL3.5 with Keras 3: JAX, PyTorch, or TensorFlow
zeromodels/internvl3.5-14b
Pure-Keras 3 conversion of OpenGVLab/InternVL3_5-14B-HF for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX. This is a InternVL3.5 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: InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency (arXiv:2508.18265) · HF Papers
Paper: Qwen3 Technical Report (arXiv:2505.09388) · HF Papers
Paper: YaRN: Efficient Context Window Extension of Large Language Models (arXiv:2309.00071) · HF Papers
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from PIL import Image
from zeromodels.models.internvl import InternVLConditionalGenerate, InternVLProcessor
model = InternVLConditionalGenerate.from_weights("zeromodels/internvl3.5-14b")
processor = InternVLProcessor.from_weights("zeromodels/internvl3.5-14b")
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("zeromodels/<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: apache-2.0 (per the upstream model card).