metadata
license: mit
pipeline_tag: text-generation
tags:
- ocean
- text-generation-inference
- oceangpt
language:
- en
datasets:
- zjunlp/OceanBench
💡 Model description
This repo contains a large language model (OceanGPT) for ocean science tasks trained with KnowLM. It should be noted that the OceanGPT is constantly being updated, so the current model is not the final version.
OceanGPT-2B is based on MiniCPM-2B and trained on a bilingual dataset in Chinese and English.
🔍 Intended uses
You can download the model to generate responses or contact the email for the online test demo.
🛠️ How to use OceanGPT
We wil provide several examples soon and you can modify the input according to your needs.
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
path = 'zjunlp/OceanGPT-2B-v0.1'
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)
responds, history = model.chat(tokenizer, "Which is the largest ocean in the world?", temperature=0.8, top_p=0.8)
print(responds)
🛠️ How to evaluate your model in OceanBench
We wil provide several examples soon and you can modify the input according to your needs.
*Note: We are conducting the final checks on OceanBench and will be uploading it to Hugging Face soon.
>>> from datasets import load_dataset
>>> dataset = load_dataset("zjunlp/OceanBench")
📚 How to cite
@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}
}