OceanGPT (沧渊): A Large Language Model for Ocean Science Tasks

Project • Paper • Models • Web • Overview • Quickstart • Citation

Table of Contents

• What's New
• Overview
• Quickstart
• Models
• Citation

🔔News

• 2024-07-04, we release OceanGPT-14B/2B-v0.1 and OceanGPT-7B-v0.2 based on Qwen and MiniCPM.
• 2024-06-04, OceanGPT is accepted by ACL 2024. 🎉🎉
• 2023-10-04, we release the paper "OceanGPT: A Large Language Model for Ocean Science Tasks" and release OceanGPT-7B-v0.1 based on LLaMA2.
• 2023-05-01, we launch the OceanGPT (沧渊) project.

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🌟Overview

This is the OceanGPT (沧渊) project, which aims to build LLMs for ocean science tasks.

⏩Quickstart

conda create -n py3.11 python=3.11
conda activate py3.11
pip install -r requirements.txt

Download the model

Download from HuggingFace

git lfs install
git clone https://huggingface.co/zjunlp/OceanGPT-14B-v0.1

or

huggingface-cli download --resume-download zjunlp/OceanGPT-14B-v0.1 --local-dir OceanGPT-14B-v0.1 --local-dir-use-symlinks False

Download from WiseModel

git lfs install
git clone https://www.wisemodel.cn/zjunlp/OceanGPT-14B-v0.1.git

Download from ModelScope

git lfs install
git clone https://www.modelscope.cn/ZJUNLP/OceanGPT-14B-v0.1.git

Inference

Inference by HuggingFace

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

device = "cuda" # the device to load the model onto
path = 'YOUR-MODEL-PATH'

model = AutoModelForCausalLM.from_pretrained(
    path,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(path)

prompt = "Which is the largest ocean in the world?"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Inference by vllm

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

path = 'YOUR-MODEL-PATH'

tokenizer = AutoTokenizer.from_pretrained(path)

prompt = "Which is the largest ocean in the world?"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

sampling_params = SamplingParams(temperature=0.8, top_k=50)
llm = LLM(model=path)

response = llm.generate(text, sampling_params)

📌Models

Model Name	HuggingFace	WiseModel	ModelScope
OceanGPT-14B-v0.1 (based on Qwen)	14B	14B	14B
OceanGPT-7B-v0.2 (based on Qwen)	7B	7B	7B
OceanGPT-2B-v0.1 (based on MiniCPM)	2B	2B	2B
OceanGPT-V	To be released	To be released	To be released

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🌻Acknowledgement

OceanGPT (沧渊) is trained based on the open-sourced large language models including Qwen, MiniCPM, LLaMA. Thanks for their great contributions!

Limitations

• The model may have hallucination issues.

• 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.

🚩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}
}

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