---
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-14B is based on Qwen1.5-14B 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
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "zjunlp/OceanGPT-14B-v0.1",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("zjunlp/OceanGPT-14B-v0.1")

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]
```

## 🛠️ 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}
}
```