Update README.md

README.md

@@ -11,42 +11,95 @@ 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-2B is based on MiniCPM-2B and trained on a bilingual dataset in Chinese and English.
-## 🔍 Intended uses
-You can download the model to generate responses or contact the [email]([email protected]) for the online test demo.
+<div align="center">
+<img src="logo.jpg" width="300px">
 
-## 🛠️ How to use OceanGPT
-We wil provide several examples soon and you can modify the input according to your needs.
+**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="#quickstart">Quickstart</a> •
+  <a href="#citation">Citation</a>
+</p>
+
+
+</div>
+
+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.
+
+
+## ⏩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
-
-path = 'zjunlp/OceanGPT-2B-v0.1'
+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)
-model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)
 
-responds, history = model.chat(tokenizer, "Which is the largest ocean in the world?", temperature=0.8, top_p=0.8)
-print(responds)
+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
+## 📌Models
 
-We wil provide several examples soon and you can modify the input according to your needs.
+| 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                                                                          |
+---
 
-*Note: We are conducting the final checks on OceanBench and will be uploading it to Hugging Face soon. 
+## 🌻Acknowledgement
 
-```python
->>> from datasets import load_dataset
+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!
 
->>> dataset = load_dataset("zjunlp/OceanBench")
-```
 
-## 📚 How to cite
+### 🚩Citation
+
+Please cite the following paper if you use OceanGPT in your work.
 
 ```bibtex
 @article{bi2023oceangpt,
@@ -55,4 +108,5 @@ We wil provide several examples soon and you can modify the input according to y
   journal={arXiv preprint arXiv:2310.02031},
   year={2023}
 }
+
 ```