zjunlp
/

OceanGPT-14B-v0.1

@@ -1,81 +1,137 @@
----
-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
-import torch
-device = "cuda" # the device to load the model onto
-model = AutoModelForCausalLM.from_pretrained(
-    "zjunlp/OceanGPT-14B-v0.1",
-    torch_dtype=torch.bfloat16,
-    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}
-}
-```

+---
+license: mit
+pipeline_tag: text-generation
+tags:
+- ocean
+- text-generation-inference
+- oceangpt
+language:
+- en
+datasets:
+- zjunlp/OceanBench
+---
+<div align="center">
+<img src="figs/logo.jpg" width="300px">
+**OceanGPT: 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/#model">Web</a> •
+  <a href="#overview">Overview</a> •
+  <a href="#quickstart">Quickstart</a> •
+  <a href="#citation">Citation</a>
+</p>
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+![](https://img.shields.io/badge/PRs-Welcome-red)
+</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.
+## Table of Contents
+- <a href="#news">What's New</a>
+- <a href="#overview">Overview</a>
+- <a href="#quickstart">Quickstart</a>
+- <a href="#models">Models</a>
+- <a href="#citation">Citation</a>
+## 🔔News
+- **2024-07-04, we release OceanGPT-14B/2B-v0.1 and OceanGPT-7B-v0.2 based on Qwen and MiniCPM.**
+- **2024-06-04, [OceanGPT](https://arxiv.org/abs/2310.02031) is accepted by ACL 2024. 🎉🎉**
+- **2023-10-04, we release the paper "[OceanGPT: A Large Language Model for Ocean Science Tasks](https://arxiv.org/abs/2310.02031)" and release OceanGPT-7B-v0.1 based on LLaMA2.**
+- **2023-05-01, we launch the OceanGPT project.**
+---
+## 🌟Overview
+This is the OceanGPT project, which aims to build LLMs for ocean science tasks.
+<div align="center">
+<img src="figs/overview.png" width="60%">
+</div>
+## ⏩Quickstart
+### Download the model
+Download the model: [OceanGPT-14B-v0.1](https://huggingface.co/zjunlp/OceanGPT-14B-v0.1) or [
+OceanGPT-7b-v0.2](https://huggingface.co/zjunlp/OceanGPT-7b-v0.2)
+```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>   |
+| OceanGPT-V  | To be released                                                                    | To be released                                                                         | To be released                                                                          |
+---
+## 🌻Acknowledgement
+OceanGPT 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!
+### 🚩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}
+}
+```