---
license: mit
pipeline_tag: text-generation
tags:
- ocean
- text-generation-inference
- oceangpt
language:
- en
- zh
datasets:
- zjunlp/OceanInstruct
---
OceanGPT-2B-v0.1 is based on MiniCPM-2B and has been trained on a bilingual dataset in the ocean domain, covering both Chinese and English.
- ❗**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: [OceanGPT-2B-v0.1](https://huggingface.co/zjunlp/OceanGPT-2B-v0.1)
```shell
git lfs install
git clone https://huggingface.co/zjunlp/OceanGPT-2B-v0.1
```
or
```
huggingface-cli download --resume-download zjunlp/OceanGPT-2B-v0.1 --local-dir OceanGPT-2B-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) | 14B | 14B | 14B |
| OceanGPT-7B-v0.2 (based on Qwen) | 7B | 7B | 7B |
| OceanGPT-2B-v0.1 (based on MiniCPM) | 2B | 2B | 2B |
## 🌻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.
- 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.
```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}
}
``` 
**OceanGPT(沧渊): A Large Language Model for Ocean Science Tasks**