Update README.md

README.md

@@ -5,12 +5,12 @@ language:
 pipeline_tag: text-generation
 inference: false
 ---
-# Baichuan-13B
+# Baichuan-13B-Base
 
 <!-- Provide a quick summary of what the model is/does. -->
 
 ## 介绍
-Baichuan-13B 是由百川智能继 [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) 之后开发的包含 130 亿参数的开源可商用的大规模语言模型，在标准的中文和英文 benchmark上均取得同尺寸最好的效果。本次发布包含有预训练 (Baichuan-13B-Base) 和对齐 (Baichuan-13B-Chat) 两个版本。Baichuan-13B 有如下几个特点：
+Baichuan-13B-Base 是由百川智能继 [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B) 之后开发的包含 130 亿参数的开源可商用的大规模语言模型，在标准的中文和英文 benchmark上均取得同尺寸最好的效果。本次发布包含有预训练 (Baichuan-13B-Base) 和对齐 (Baichuan-13B-Chat) 两个版本。Baichuan-13B 有如下几个特点：
 
 1. **开源可商用百亿级别中文语言模型**：Baichuan-13B-Base 是免费开源可商用的百亿级别中文预训练语言模型。包含有130亿参数，没有经过任何 Instruction Tuning 或者针对 benchmark 的优化，纯净、高可定制。弥补了在中文领域缺乏 100 亿以上高可用中文预训练大模型的短板。
 2. **更大尺寸、更多数据**：在 Baichuan-7B 的基础上进一步扩大参数量到 130 亿，并且在高质量的语料上训练了 1.4 万亿 tokens，是当前开源 13B 尺寸下训练数据量最多的模型。支持中英双语，使用 [ALiBi](https://arxiv.org/abs/2108.12409) 位置编码，上下文窗口长度为 4096。
@@ -30,11 +30,11 @@ Baichuan-13B is an open-source, commercially available large-scale language mode
 
 ## How to Get Started with the Model
 
-如下是一个使用Baichuan-13B进行1-shot推理的任务，根据作品给出作者名，正确输出为"夜雨寄北->李商隐"
+如下是一个使用Baichuan-13B-Base进行1-shot推理的任务，根据作品给出作者名，正确输出为"夜雨寄北->李商隐"
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-13B", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-13B-Base", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-13B", device_map="auto", trust_remote_code=True)
 inputs = tokenizer('登鹳雀楼->王之涣\n夜雨寄北->', return_tensors='pt')
 inputs = inputs.to('cuda:0')
@@ -42,11 +42,11 @@ pred = model.generate(**inputs, max_new_tokens=64,repetition_penalty=1.1)
 print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
 ```
 
-The following is a task of performing 1-shot inference using Baichuan-13B, where the author's name is given based on the work, with the correct output being "One Hundred Years of Solitude->Gabriel Garcia Marquez"
+The following is a task of performing 1-shot inference using Baichuan-13B-Base, where the author's name is given based on the work, with the correct output being "One Hundred Years of Solitude->Gabriel Garcia Marquez"
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-13B", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-13B-Base", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-13B", device_map="auto", trust_remote_code=True)
 inputs = tokenizer('Hamlet->Shakespeare\nOne Hundred Years of Solitude->', return_tensors='pt')
 inputs = inputs.to('cuda:0')