modify: update model name

README.md

@@ -5,49 +5,49 @@ language:
 pipeline_tag: text-generation
 inference: false
 ---
-# baichuan-7B
+# Baichuan-7B
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-baichuan-7B是由百川智能开发的一个开源的大规模预训练模型。基于Transformer结构，在大约1.2万亿tokens上训练的70亿参数模型，支持中英双语，上下文窗口长度为4096。在标准的中文和英文权威benchmark（C-EVAL/MMLU）上均取得同尺寸最好的效果。
+Baichuan-7B是由百川智能开发的一个开源的大规模预训练模型。基于Transformer结构，在大约1.2万亿tokens上训练的70亿参数模型，支持中英双语，上下文窗口长度为4096。在标准的中文和英文权威benchmark（C-EVAL/MMLU）上均取得同尺寸最好的效果。
 
-如果希望使用baichuan-7B（如进行推理、Finetune等），我们推荐使用配套代码库[baichuan-7B](https://
-.com/baichuan-inc/baichuan-7B)。
+如果希望使用Baichuan-7B（如进行推理、Finetune等），我们推荐使用配套代码库[Baichuan-7B](https://
+.com/Baichuan-inc/Baichuan-7B)。
 
-baichuan-7B is an open-source large-scale pre-trained model developed by Baichuan Intelligent Technology. Based on the Transformer architecture, it is a model with 7 billion parameters trained on approximately 1.2 trillion tokens. It supports both Chinese and English, with a context window length of 4096. It achieves the best performance of its size on standard Chinese and English authoritative benchmarks (C-EVAL/MMLU).
+Baichuan-7B is an open-source large-scale pre-trained model developed by Baichuan Intelligent Technology. Based on the Transformer architecture, it is a model with 7 billion parameters trained on approximately 1.2 trillion tokens. It supports both Chinese and English, with a context window length of 4096. It achieves the best performance of its size on standard Chinese and English authoritative benchmarks (C-EVAL/MMLU).
 
-If you wish to use baichuan-7B (for inference, finetuning, etc.), we recommend using the accompanying code library [baichuan-7B](https://github.com/baichuan-inc/baichuan-7B).
+If you wish to use Baichuan-7B (for inference, finetuning, etc.), we recommend using the accompanying code library [Baichuan-7B](https://github.com/Baichuan-inc/Baichuan-7B).
 
-## Why use baichuan-7B
+## Why use Baichuan-7B
 
-- 在同尺寸模型中baichuan-7B达到了目前SOTA的水平，参考下面MMLU指标
-- baichuan-7B使用自有的中英文双语语料进行训练，在中文上进行优化，在C-Eval达到SOTA水平
-- 不同于LLaMA完全禁止商业使用，baichuan-7B使用更宽松的开源协议，允许用于商业目的
+- 在同尺寸模型中Baichuan-7B达到了目前SOTA的水平，参考下面MMLU指标
+- Baichuan-7B使用自有的中英文双语语料进行训练，在中文上进行优化，在C-Eval达到SOTA水平
+- 不同于LLaMA完全禁止商业使用，Baichuan-7B使用更宽松的开源协议，允许用于商业目的
 
-- Among models of the same size, baichuan-7B has achieved the current state-of-the-art (SOTA) level, as evidenced by the following MMLU metrics.
-- baichuan-7B is trained on proprietary bilingual Chinese-English corpora, optimized for Chinese, and achieves SOTA performance on C-Eval.
-- Unlike LLaMA, which completely prohibits commercial use, baichuan-7B employs a more lenient open-source license, allowing for commercial purposes.
+- Among models of the same size, Baichuan-7B has achieved the current state-of-the-art (SOTA) level, as evidenced by the following MMLU metrics.
+- Baichuan-7B is trained on proprietary bilingual Chinese-English corpora, optimized for Chinese, and achieves SOTA performance on C-Eval.
+- Unlike LLaMA, which completely prohibits commercial use, Baichuan-7B employs a more lenient open-source license, allowing for commercial purposes.
 
 ## How to Get Started with the Model
 
-如下是一个使用baichuan-7B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-7B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-7B", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("baichuan-inc/baichuan-7B", device_map="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-7B", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-7B", 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')
 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-7B, 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-7B, 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-7B", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("baichuan-inc/baichuan-7B", device_map="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-7B", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-7B", device_map="auto", trust_remote_code=True)
 inputs = tokenizer('Hamlet->Shakespeare\nOne Hundred Years of Solitude->', return_tensors='pt')
 inputs = inputs.to('cuda:0')
 pred = model.generate(**inputs, max_new_tokens=64,repetition_penalty=1.1)
@@ -63,7 +63,7 @@ print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
 - **Developed by:** 百川智能(Baichuan Intelligent Technology)
 - **Email**: [email protected]
 - **Language(s) (NLP):** Chinese/English
-- **License:** [baichuan-7B License](https://huggingface.co/baichuan-inc/baichuan-7B/blob/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
+- **License:** [Baichuan-7B License](https://huggingface.co/baichuan-inc/Baichuan-7B/blob/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)
 
 ### Model Sources
 
@@ -107,9 +107,9 @@ The specific parameters are as follows:
 ### Downstream Use 
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-我们同时开源出了和本模型配套的训练代码，允许进行高效的Finetune用于下游任务，具体参见[baichuan-7B](https://github.com/baichuan-inc/baichuan-7B)。
+我们同时开源出了和本模型配套的训练代码，允许进行高效的Finetune用于下游任务，具体参见[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。
 
-We have also open-sourced the training code that accompanies this model, allowing for efficient finetuning for downstream tasks. For more details, please refer to [baichuan-7B](https://github.com/baichuan-inc/baichuan-7B).
+We have also open-sourced the training code that accompanies this model, allowing for efficient finetuning for downstream tasks. For more details, please refer to [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B).
 
 ### Out-of-Scope Use
 
@@ -122,15 +122,15 @@ Production use without adequate assessment of risks and mitigation; any use case
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-baichuan-7B可能会产生事实上不正确的输出，不应依赖它产生事实上准确的信息。baichuan-7B是在各种公共数据集上进行训练的。尽管我们已经做出了巨大的努力来清洗预训练数据，但这个模型可能会生成淫秽、偏见或其他冒犯性的输出。
+Baichuan-7B可能会产生事实上不正确的输出，不应依赖它产生事实上准确的信息。Baichuan-7B是在各种公共数据集上进行训练的。尽管我们已经做出了巨大的努力来清洗预训练数据，但这个模型可能会生成淫秽、偏见或其他冒犯性的输出。
 
-baichuan-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. baichuan-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+Baichuan-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. Baichuan-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
 
 ## Training Details
 
-训练具体设置参见[baichuan-7B](https://github.com/baichuan-inc/baichuan-7B)。
+训练具体设置参见[Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B)。
 
-For specific training settings, please refer to [baichuan-7B](https://github.com/baichuan-inc/baichuan-7B).
+For specific training settings, please refer to [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B).
 
 ## Evaluation
 
@@ -155,7 +155,7 @@ For specific training settings, please refer to [baichuan-7B](https://github.com
 | Aquila-7B<sup>*</sup>       | 25.5    | 25.2      | 25.6 | 24.6            | 25.2       | 26.6   |
 | BLOOM-7B                    | 22.8    | 20.2      | 21.8 | 23.3            | 23.9       | 23.3   |
 | BLOOMZ-7B                   | 35.7    | 25.8      | 31.3 | 43.5            | 36.6       | 35.6   |
-| **baichuan-7B**             | 42.8    | 31.5      | 38.2 | 52.0            | 46.2       | 39.3   |
+| **Baichuan-7B**             | 42.8    | 31.5      | 38.2 | 52.0            | 46.2       | 39.3   |
 
 
 #### Gaokao
@@ -175,7 +175,7 @@ For specific training settings, please refer to [baichuan-7B](https://github.com
 | BLOOM-7B                | 26.96           |
 | BLOOMZ-7B               | 28.72           |
 | Aquila-7B<sup>*</sup>               | 24.39           |
-| **baichuan-7B**        | **36.24**           |
+| **Baichuan-7B**        | **36.24**           |
 
 
 #### AGIEval
@@ -193,7 +193,7 @@ For specific training settings, please refer to [baichuan-7B](https://github.com
 | BLOOM-7B                | 26.55           |
 | BLOOMZ-7B               | 30.27           |
 | Aquila-7B<sup>*</sup>               | 25.58           |
-| **baichuan-7B**        | **34.44**           |
+| **Baichuan-7B**        | **34.44**           |
 
 <sup>*</sup>其中Aquila模型来源于[智源官方网站](https://model.baai.ac.cn/model-detail/100098)，仅做参考
 
@@ -216,7 +216,7 @@ We adopted the [open-source]((https://github.com/hendrycks/test)) evaluation sch
 | BLOOMZ 7B<sup>0</sup>                 |       31.3 |      42.1       | 34.4 | 39.0  |  36.1   |
 | moss-moon-003-base (16B)<sup>0</sup>   |       24.2 |      22.8       | 22.4 | 24.4  |  23.6   |
 | moss-moon-003-sft (16B)<sup>0</sup>    |       30.5 |      33.8       | 29.3 | 34.4  |  31.9   |
-| **baichuan-7B<sup>0</sup>**            |       38.4 |      48.9       | 35.6 | 48.1  |  42.3   |
+| **Baichuan-7B<sup>0</sup>**            |       38.4 |      48.9       | 35.6 | 48.1  |  42.3   |
 
 The superscript in the Model column indicates the source of the results.
 ```
@@ -226,4 +226,4 @@ The superscript in the Model column indicates the source of the results.
 ```
 
 ## Our Group
-[WeChat](https://github.com/baichuan-inc/baichuan-7B/blob/main/media/wechat.jpeg?raw=true)
+[WeChat](https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true)