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--- |
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license: mit |
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pipeline_tag: text-generation |
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tags: |
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- ocean |
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- text-generation-inference |
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- oceangpt |
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language: |
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- en |
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- zh |
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datasets: |
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- zjunlp/OceanInstruct |
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--- |
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<div align="center"> |
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<img src="logo.jpg" width="300px"> |
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**OceanGPT(沧渊): A Large Language Model for Ocean Science Tasks** |
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<p align="center"> |
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<a href="https://github.com/zjunlp/OceanGPT">Project</a> • |
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<a href="https://arxiv.org/abs/2310.02031">Paper</a> • |
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<a href="https://huggingface.co/collections/zjunlp/oceangpt-664cc106358fdd9f09aa5157">Models</a> • |
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<a href="http://oceangpt.zjukg.cn/">Web</a> • |
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<a href="#quickstart">Quickstart</a> • |
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<a href="#citation">Citation</a> |
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</p> |
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</div> |
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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. |
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- ❗**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.** |
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## ⏩Quickstart |
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### Download the model |
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Download the model: [OceanGPT-2B-v0.1](https://huggingface.co/zjunlp/OceanGPT-2B-v0.1) |
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```shell |
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git lfs install |
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git clone https://huggingface.co/zjunlp/OceanGPT-2B-v0.1 |
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``` |
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or |
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``` |
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huggingface-cli download --resume-download zjunlp/OceanGPT-2B-v0.1 --local-dir OceanGPT-2B-v0.1 --local-dir-use-symlinks False |
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``` |
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### Inference |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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device = "cuda" # the device to load the model onto |
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path = 'YOUR-MODEL-PATH' |
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model = AutoModelForCausalLM.from_pretrained( |
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path, |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(path) |
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prompt = "Which is the largest ocean in the world?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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``` |
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## 📌Models |
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| Model Name | HuggingFace | WiseModel | ModelScope | |
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|-------------------|-----------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------| |
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| 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> | |
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| 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> | |
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| 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> | |
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## 🌻Acknowledgement |
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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! |
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## Limitations |
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- The model may have hallucination issues. |
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- We did not optimize the identity and the model may generate identity information similar to that of Qwen/MiniCPM/LLaMA/GPT series models. |
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- The model's output is influenced by prompt tokens, which may result in inconsistent results across multiple attempts. |
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- 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. |
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### 🚩Citation |
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Please cite the following paper if you use OceanGPT in your work. |
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```bibtex |
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@article{bi2023oceangpt, |
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title={OceanGPT: A Large Language Model for Ocean Science Tasks}, |
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author={Bi, Zhen and Zhang, Ningyu and Xue, Yida and Ou, Yixin and Ji, Daxiong and Zheng, Guozhou and Chen, Huajun}, |
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journal={arXiv preprint arXiv:2310.02031}, |
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year={2023} |
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} |
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``` |
