Update README.md

README.md

@@ -1,3 +1,104 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- zh
+pipeline_tag: text-generation
+---
+---
+<div style="text-align:center">
+<h2>📈 CFGPT2: Chinese Financial Assistant with Large Language Model (CFGPT2-7B)</h2>
+</div>
+
+## Introduction
+
+We introduce **CFGPT2**, an open-source language model trained by firstly further pretraining InternLM2 on collected and cleaned Chinese finance text data (CFData-pt), including financial domain-specific data (announcement, finance articles, finance exams, finance news, finance research papers) and general data (Wikipedia), and secondly fine-tuning with knowledge-intensive instruction tuning data (CFData-sft). 
+As for preliminary evaluation, we use CFBenchmark. 
+CFGPT outperforms the baselines on objective and subjective financial tasks compared to several baseline models with similar parameters. 
+
+In this repository, we will share the CFGPT2-7B.
+
+- [CFGPT2-7B](https://huggingface.co/TongjiFinLab/CFGPT2-7B): The 7B version of our CFGPT2 model.
+
+## How to Use
+
+**1. Prepare the code and the environment**
+
+Clone [CFGPT](https://github.com/TongjiFinLab/CFGPT.git) repository, create a Python environment, and activate it via the following command
+```bash
+git clone https://github.com/TongjiFinLab/CFGPT.git
+cd CFGPT
+conda create -n env_name python=3.10   
+source activate env_name 
+pip install -r requirements.txt
+```
+
+**2. Use CFGPT2-7B**
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+base_model = 'TongjiFinLab/CFGPT2'
+device_map = 'cuda:0'
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    trust_remote_code=True, 
+    torch_dtype=torch.bfloat16, 
+    device_map=device_map
+)
+model = model.eval()
+
+history = []
+
+query = "能否给我用中文写一篇有关新能源汽车领域的市场分析报告，写的长一些，细致一些"
+
+response, history = model.chat(tokenizer = tokenizer, query = query, history = history, max_new_tokens = 1024, do_sample = True, temperature = 0.8, top_p = 0.8, repetition_penalty=1.1, meta_instruction='')
+print(response)
+```
+
+## 简介
+
+**CFGPT2**是一个开源的语言模型，首先通过在收集和清理的中国金融文本数据（CFData-pt）上利用InternLM2进行继续预训练，包括金融领域特定数据（公告、金融文章、金融考试、金融新闻、金融研究论文）和通用数据（维基百科），然后使用知识密集的指导调整数据（CFData-sft）进行微调。
+我们使用CFBenchmark进行初步评估。与几个具有相似参数的基线模型相比，CFGPT在多个金融任务上表现优越。
+
+在这个仓库中，我们将分享以下CFGPT2-7B模型。
+
+- [CFGPT2-7B](https://huggingface.co/TongjiFinLab/CFGPT2-7B): CFGPT2的7B版本。
+
+## 如何使用
+
+**1. 准备代码和环境**
+
+克隆[CFGPT](https://github.com/TongjiFinLab/CFGPT.git)的仓库，创建一个Python环境，并通过以下命令激活它：
+```bash
+git clone https://github.com/TongjiFinLab/CFGPT.git
+cd CFGPT
+conda create -n env_name python=3.10   
+source activate env_name 
+pip install -r requirements.txt
+```
+
+**2. 使用 CFGPT2-7B**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+base_model = 'TongjiFinLab/CFGPT2'
+device_map = 'cuda:0'
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    trust_remote_code=True, 
+    torch_dtype=torch.bfloat16, 
+    device_map=device_map
+)
+model = model.eval()
+
+history = []
+
+query = "能否给我用中文写一篇有关新能源汽车领域的市场分析报告，写的长一些，细致一些"
+
+response, history = model.chat(tokenizer = tokenizer, query = query, history = history, max_new_tokens = 1024, do_sample = True, temperature = 0.8, top_p = 0.8, repetition_penalty=1.1, meta_instruction='')
+print(response)
+```