Update README.md

README.md

@@ -19,7 +19,6 @@ Baichuan-13B-Chat为Baichuan-13B系列模型中对齐后的版本，预训练模
   3. **更高效的推理**：为了支持更广大用户的使用，我们本次同时开源了INT8和INT4的量化版本，在几乎没有效果损失的情况下可以很方便的将模型部署在如3090等消费机显卡上。
   4. **开源免费可商用**：Baichuan-13B不仅对学术研究完全开放，开发者也仅需邮件申请并获得官方商用许可后，即可以免费商用。
 
-## Introduction
 Baichuan-13B-Chat is the aligned version in the Baichuan-13B series of models, and the pre-trained model can be found at [Baichuan-13B-Base](https://github.com/baichuan-inc/Baichuan-13B-Base).
 
 [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B) is an open-source, commercially usable large-scale language model developed by Baichuan Intelligence, following [Baichuan-7B](https://github.com/baichuan-inc/baichuan-7B). With 13 billion parameters, it achieves the best performance in standard Chinese and English benchmarks among models of its size. This release includes two versions: pre-training (Baichuan-13B-Base) and alignment (Baichuan-13B-Chat). Baichuan-13B has the following features:
@@ -30,7 +29,7 @@ Baichuan-13B-Chat is the aligned version in the Baichuan-13B series of models, a
   4. **Open-source, free, and commercially usable**: Baichuan-13B is not only fully open to academic research, but developers can also use it for free commercially after applying for and receiving official commercial permission via email.
 
 
-## How to Get Started with the Model
+## 使用方式
 
 如下是一个使用Baichuan-13B-Chat进行对话的示例，正确输出为"乔戈里峰。世界第二高峰———乔戈里峰西方登山者称其为k2峰，海拔高度是8611米，位于喀喇昆仑山脉的中巴边境上"
 ```python
@@ -60,9 +59,9 @@ response = model.chat(tokenizer, messages)
 print(response)
 ```
 
-## Model Details
+## 模型详情
 
-### Model Description
+### 模型简述
 
 <!-- Provide a longer summary of what this model is. -->
 
@@ -71,7 +70,7 @@ print(response)
 - **Language(s) (NLP):** Chinese/English
 - **License:** [Baichuan-13B License]()
 
-### Model Sources
+### 模型结构
 
 <!-- Provide the basic links for the model. -->
 
@@ -101,46 +100,42 @@ The specific parameters are as follows:
 | Baichuan-7B             | 4,096  | 32       | 32   | 64,000    | 7,000,559,616  | 1.2万亿           | [RoPE](https://arxiv.org/abs/2104.09864)    | 4,096    |
 | Baichuan-13B             | 5,120 | 40       | 40  | 64,000    | 13,264,901,120   | 1.4万亿           | [ALiBi](https://arxiv.org/abs/2108.12409)    | 4,096
 
-## Uses
+## 使用须知
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Downstream Use 
+### 下游使用
 
 <!-- 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-13B](https://github.com/baichuan-inc/Baichuan-13B)。
 
 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-13B](https://github.com/baichuan-inc/Baichuan-13B).
 
-### Out-of-Scope Use
+### 注意事项
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 在没有充分评估风险和采取缓解措施的情况下投入生产使用；任何可能被视为不负责任或有害的使用案例。
 
 Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
 
-## Bias, Risks, and Limitations
-
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 Baichuan-13B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-13B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-13B can produce factually incorrect output, and should not be relied on to produce factually accurate information. Baichuan-13B 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
+## 训练详情
 
 训练具体设置参见[Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B)。
 
 For specific training settings, please refer to [Baichuan-13B](https://github.com/baichuan-inc/Baichuan-13B).
 
-## Evaluation
-
-# Benchmark结果
+## 测评结果
 
 我们在各个 benchmark 下进行了`5-shot`评测，所采用的方法和 [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B/) 项目中相同。结果如下���
 
 We conducted a `5-shot` evaluation under various benchmarks, using the same method as in the [Baichuan-7B](https://github.com/baichuan-inc/Baichuan-7B/) project. The results are as follows:
-## C-Eval
+### C-Eval
 
 | Model 5-shot            | STEM  | Social Sciences | Humanities | Others | Average |
 |-------------------------|-------|-----------------|------------|--------|---------|
@@ -155,7 +150,7 @@ We conducted a `5-shot` evaluation under various benchmarks, using the same meth
 | **Baichuan-13B-Chat**   | **43.7** | **64.6** | **56.2**    | **49.2** | **51.5** |
 > *说明：表中各个模型的结果是使用统一的评估代码得到。[InternLM-7B](https://huggingface.co/internlm/internlm-7b) 汇报使用 [OpenCompass](https://opencompass.org.cn/rank) 工具评估的C-Eval平均值为 53.4，我们使用 OpenCompass 评估 InternLM-7B 的平均值为 51.6
 
-## MMLU
+### MMLU
 
 | Model 5-shot            | STEM  | Social Sciences | Humanities | Others | Average |
 |-------------------------|-------|-----------------|------------|--------|---------|
@@ -170,7 +165,7 @@ We conducted a `5-shot` evaluation under various benchmarks, using the same meth
 | **Baichuan-13B-Chat**   | **40.9** | **60.9** | **48.8**    | **59.0** | **52.1** |
 
 
-## CMMLU
+### CMMLU
 
 | Model 5-shot            | STEM  | Humanities | Social Sciences | Others | China Specific | Average |
 |-------------------------|-------|------------|-----------------|--------|----------------|---------|
@@ -186,5 +181,5 @@ We conducted a `5-shot` evaluation under various benchmarks, using the same meth
 
 > 说明：CMMLU是一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力。我们采用了其官方的[评测方案](https://github.com/haonan-li/CMMLU)。
 
-## Our Group
+## 微信群组
 ![WeChat](https://github.com/baichuan-inc/baichuan-7B/blob/main/media/wechat.jpeg?raw=true)