OceanGPT(沧渊): A Large Language Model for Ocean Science Tasks
OceanGPT-o is based on Qwen2.5-VL and has been trained on an English and Chinese dataset in the ocean domain (recent update 20250514) .
Please note that the models and data in this repository are updated regularly to fix errors. The latest update date will be added to the README for your reference.
- ❗We will continue to update.
- ❗Disclaimer: This project is purely an academic exploration rather than a product. Please be aware that due to the inherent limitations of large language models, there may be issues such as hallucinations.
⏩Quickstart
Download the model
Download the model: zjunlp/OceanGPT-o-7B
git lfs install
git clone https://huggingface.co/zjunlp/OceanGPT-o-7B
or
huggingface-cli download --resume-download zjunlp/OceanGPT-o-7B --local-dir OceanGPT-o-7B --local-dir-use-symlinks False
Qwen2.5-VL offers a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
# It's highly recommanded to use `[decord]` feature for faster video loading.
pip install qwen-vl-utils[decord]==0.0.8
Inference
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"zjunlp/OceanGPT-o-7B", torch_dtype=torch.bfloat16, device_map="auto"
)
processor = AutoProcessor.from_pretrained("zjunlp/OceanGPT-o-7B")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "file:///path/to/your/image.jpg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
🌻Acknowledgement
OceanGPT (沧渊) is trained based on the open-sourced large language models including Qwen, MiniCPM, LLaMA.
OceanGPT is trained based on the open-sourced data and tools including Moos, UATD, Forward-looking Sonar Detection Dataset, NKSID, SeabedObjects-KLSG, Marine Debris.
Thanks for their great contributions!
Limitations
-
The model may have hallucination issues.
-
Due to limited computational resources, OceanGPT-o currently only supports natural language generation for certain types of sonar images and ocean science images.
-
We did not optimize the identity and the model may generate identity information similar to that of Qwen/MiniCPM/LLaMA/GPT series models.
-
The model's output is influenced by prompt tokens, which may result in inconsistent results across multiple attempts.
-
The model requires the inclusion of specific simulator code instructions for training in order to possess simulated embodied intelligence capabilities (the simulator is subject to copyright restrictions and cannot be made available for now), and its current capabilities are quite limited.
🚩Citation
Please cite the following paper if you use OceanGPT in your work.
@article{bi2023oceangpt,
title={OceanGPT: A Large Language Model for Ocean Science Tasks},
author={Bi, Zhen and Zhang, Ningyu and Xue, Yida and Ou, Yixin and Ji, Daxiong and Zheng, Guozhou and Chen, Huajun},
journal={arXiv preprint arXiv:2310.02031},
year={2023}
}