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---
license: mit
pipeline_tag: text-generation
tags:
- ocean
- text-generation-inference
- oceangpt
language:
- en
- zh
datasets:
- zjunlp/OceanInstruct
---
<div align="center">
<img src="logo.jpg" width="300px">
**OceanGPT(沧渊): A Large Language Model for Ocean Science Tasks**
<p align="center">
<a href="https://github.com/zjunlp/OceanGPT">Project</a> •
<a href="https://arxiv.org/abs/2310.02031">Paper</a> •
<a href="https://huggingface.co/collections/zjunlp/oceangpt-664cc106358fdd9f09aa5157">Models</a> •
<a href="http://oceangpt.zjukg.cn/">Web</a> •
<a href="#quickstart">Quickstart</a> •
<a href="#citation">Citation</a>
</p>
</div>
OceanGPT-14B-v0.1 is based on Qwen1.5-14B and has been trained on a bilingual dataset in the ocean domain, covering both Chinese and English.
## ⏩Quickstart
### Download the model
Download the model: [OceanGPT-14B-v0.1](https://huggingface.co/zjunlp/OceanGPT-14B-v0.1)
```shell
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
```
### Inference
```python
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]
```
## 📌Models
| Model Name | HuggingFace | WiseModel | ModelScope |
|-------------------|-----------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|
| OceanGPT-14B-v0.1 (based on Qwen) | <a href="https://huggingface.co/zjunlp/OceanGPT-14B-v0.1" target="_blank">14B</a> | <a href="https://wisemodel.cn/models/zjunlp/OceanGPT-14B-v0.1" target="_blank">14B</a> | <a href="https://modelscope.cn/models/ZJUNLP/OceanGPT-14B-v0.1" target="_blank">14B</a> |
| OceanGPT-7B-v0.2 (based on Qwen) | <a href="https://huggingface.co/zjunlp/OceanGPT-7b-v0.2" target="_blank">7B</a> | <a href="https://wisemodel.cn/models/zjunlp/OceanGPT-7b-v0.2" target="_blank">7B</a> | <a href="https://modelscope.cn/models/ZJUNLP/OceanGPT-7b-v0.2" target="_blank">7B</a> |
| OceanGPT-2B-v0.1 (based on MiniCPM) | <a href="https://huggingface.co/zjunlp/OceanGPT-2B-v0.1" target="_blank">2B</a> | <a href="https://wisemodel.cn/models/zjunlp/OceanGPT-2b-v0.1" target="_blank">2B</a> | <a href="https://modelscope.cn/models/ZJUNLP/OceanGPT-2B-v0.1" target="_blank">2B</a> |
## 🌻Acknowledgement
OceanGPT(沧渊) is trained based on the open-sourced large language models including [Qwen](https://huggingface.co/Qwen), [MiniCPM](https://huggingface.co/collections/openbmb/minicpm-2b-65d48bf958302b9fd25b698f), [LLaMA](https://huggingface.co/meta-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.
```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}
}
``` |