README

README.md

@@ -20,19 +20,19 @@ license: other
 
 [![evaluation](https://github.com/InternLM/InternLM/assets/22529082/f80a2a58-5ddf-471a-8da4-32ab65c8fd3b)](https://github.com/internLM/OpenCompass/)
 
-[[表情]Github Repo](https://github.com/InternLM/InternLM) • [[表情]Reporting Issues](https://github.com/InternLM/InternLM/issues/new)
+[💻Github Repo](https://github.com/InternLM/InternLM) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new)
 
 </div>
 
 
 ## Introduction
-InternLM2-1.8B is the 1.8 billion parameter version of the second generation InternLM series. In order to facilitate user use and research, InternLM2-1.8B has two versions of open-source models. They are:
+InternLM2-1.8B is the 1.8 billion parameter version of the second generation InternLM series. In order to facilitate user use and research, InternLM2-1.8B has three versions of open-source models. They are:
 
+- InternLM2-1.8B: Foundation models with high quality and high adaptation flexibility, which serve as a good starting point for downstream deep adaptations.
+- InternLM2-Chat-1.8B-SFT: Chat model after supervised fine-tuning (SFT) on InternLM2-1.8B. 
+- InternLM2-Chat-1.8B: Further aligned on top of InternLM2-Chat-1.8B-SFT through online RLHF. InternLM2-Chat-1.8B-SFT exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.
 
-- internlm2: Built upon the internlm2-base, this version has further pretrained on domain-specific corpus. It shows outstanding performance in evaluations while maintaining robust general language abilities, making it our recommended choice for most applications.
-- internlm2-chat: Optimized for conversational interaction on top of the internlm2-chat-sft through RLHF, it excels in instruction adherence, empathetic chatting, and tool invocation.
-
-The base model of InternLM2 has the following technical features:
+The InternLM2 has the following technical features:
 
 - Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval.
 - Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.
@@ -44,8 +44,8 @@ The base model of InternLM2 has the following technical features:
 
 We have evaluated InternLM2 on several important benchmarks using the open-source evaluation tool [OpenCompass](https://github.com/open-compass/opencompass). Some of the evaluation results are shown in the table below. You are welcome to visit the [OpenCompass Leaderboard](https://opencompass.org.cn/rank) for more evaluation results.
 
-| Dataset\Models | InternLM2-1.8B | InternLM2-Chat-1.8B | InternLM2-7B | InternLM2-Chat-7B |
-| --- | --- | --- | --- | --- |
+| Dataset\Models | InternLM2-1.8B | InternLM2-Chat-1.8B-SFT | InternLM2-7B | InternLM2-Chat-7B |
+| :---: | :---: | :---: | :---: | :---: |
 | MMLU | 46.9 | 47.1 | 65.8 | 63.7 |
 | AGIEval | 33.4 | 38.8 | 49.9 | 47.2 |
 | BBH | 37.5 | 35.2 | 65.0 | 61.2 |
@@ -91,12 +91,13 @@ print(output)
 The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage. To apply for a commercial license, please fill in the [application form (English)](https://wj.qq.com/s2/12727483/5dba/)/[申请表（中文）](https://wj.qq.com/s2/12725412/f7c1/). For other questions or collaborations, please contact <internlm@pjlab.org.cn>.
 
 ## 简介
-书生·浦语-1.8B (InternLM2-1.8B) 是第二代浦语模型系列的18亿参数版本。为了方便用户使用和研究，书生·浦语-1.8B (InternLM2-1.8B) 共有两个版本的开源模型，他们分别是：
+书生·浦语-1.8B (InternLM2-1.8B) 是第二代浦语模型系列的18亿参数版本。为了方便用户使用和研究，书生·浦语-1.8B (InternLM2-1.8B) 共有三个版本的开源模型，他们分别是：
 
-- internlm2: 在internlm2-base基础上，进一步在特定领域的语料上进行预训练，在评测中成绩优异，同时保持了很好的通用语言能力，是我们推荐的在大部分应用中考虑选用的优秀基座；
-- internlm2-chat：在internlm2-chat-sft基础上，经过RLHF，面向对话交互进行了优化，具有很好的指令遵循、共情聊天和调用工具等的能力。
+- InternLM2-1.8B: 具有高质量和高适应灵活性的基础模型，为下游深度适应提供了良好的起点。
+- InternLM2-Chat-1.8B-SFT：在 InternLM2-1.8B 上进行监督微调 (SFT) 后得到的对话模型。
+- InternLM2-chat-1.8B：通过在线 RLHF 在 InternLM2-Chat-1.8B-SFT 之上进一步对齐。 InternLM2-Chat-1.8B-SFT 表现出更好的指令跟随、聊天体验和函数调用，推荐下游应用程序使用。
 
-InternLM2 的基础模型具备以下的技术特点
+InternLM2 模型具备以下的技术特点
 
 - ��效支持20万字超长上下文：模型在20万字长输入中几乎完美地实现长文“大海捞针”，而且在 LongBench 和 L-Eval 等长文任务中的表现也达到开源模型中的领先水平。 
 - 综合性能全面提升：各能力维度相比上一代模型全面进步，在推理、数学、代码等方面的能力提升显著。
@@ -107,8 +108,8 @@ InternLM2 的基础模型具备以下的技术特点
 
 我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 对 InternLM2 在几个重要的评测集进行了评测 ，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://opencompass.org.cn/rank)获取更多的评测结果。
 
-| 评测集 | InternLM2-1.8B | InternLM2-Chat-1.8B | InternLM2-7B | InternLM2-Chat-7B |
-| --- | --- | --- | --- | --- |
+| 评测集 | InternLM2-1.8B | InternLM2-Chat-1.8B-SFT | InternLM2-7B | InternLM2-Chat-7B |
+| :---: | :---: | :---: | :---: | :---: |
 | MMLU | 46.9 | 47.1 | 65.8 | 63.7 |
 | AGIEval | 33.4 | 38.8 | 49.9 | 47.2 |
 | BBH | 37.5 | 35.2 | 65.0 | 61.2 |