---
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](https://github.com/zjunlp/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](bizhen_zju@zju.edu.cn) 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.

```python
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. 

```python
>>> from datasets import load_dataset

>>> dataset = load_dataset("zjunlp/OceanBench")
```

## 📚 How to cite

```bibtex
@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}
}
```