new readme

README.md

@@ -1,55 +1,80 @@
----
-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-2B is based on Llama3 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
-
-path = 'zjunlp/OceanGPT-8B-v0.1'
-tokenizer = AutoTokenizer.from_pretrained(path)
-model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)
-```
-
-## 🛠️ 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
+---
+
+## 💡 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("Qwen/Qwen1.5-14B-Chat")
+
+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}
+}
 ```