[Doc]: Update readme (#620)

2024-01-18 13:00:09 +08:00 · 2024-01-18 13:00:09 +08:00 · 0cae239483
parent ee9a22f04a
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2 changed files with 10 additions and 15 deletions
--- a/README.md
+++ b/README.md
@ -17,10 +17,7 @@
 [![license](./assets/license.svg)](./LICENSE)
 [![evaluation](./assets/compass_support.svg)](https://github.com/internLM/OpenCompass/)
 <!-- [![Documentation Status](https://readthedocs.org/projects/internlm/badge/?version=latest)](https://internlm.readthedocs.io/zh_CN/latest/?badge=latest) -->
-[📘Chat](./chat) |
-[🛠️Agent](./agent) |
-[📊Evaluation](#evaluation) |
-[👀Model](./model_cards) |
+[📘Commercial Application](#license) |
 [🤗HuggingFace](https://huggingface.co/spaces/internlm/internlm2-Chat-7B) |
 [🆕Update News](#news) |
 [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new)
@ -38,7 +35,7 @@

 InternLM2 series are released with the following features:

- **200K Context window**: Nearly perfect at finding needles in the haystack with 200K-long context, with leading performance on long-context tasks like LongBench and L-Eval. Try it with [LMDeploy](./inference/) for 200K-context inference.
+- **200K Context window**: Nearly perfect at finding needles in the haystack with 200K-long context, with leading performance on long-context tasks like LongBench and L-Eval. Try it with [LMDeploy](./chat/lmdeploy.md) for 200K-context inference.

 - **Outstanding comprehensive performance**: Significantly better than the last generation in all dimensions, especially in reasoning, math, code, chat experience, instruction following, and creative writing, with leading performance among open-source models in similar sizes. In some evaluations, InternLM2-Chat-20B may match or even surpass ChatGPT (GPT-3.5).

@ -67,7 +64,6 @@ InternLM2 series are released with the following features:
 | **InternLM2-Chat-20B-SFT**     | [🤗internlm/internlm2-chat-20b-sft](https://huggingface.co/internlm/internlm2-chat-20b-sft)         | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b-sft/summary)         | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b-sft)     | 2024-01-17   |
 | **InternLM2-Chat-20B**     | [🤗internlm/internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b)         | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b/summary)         | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b)     | 2024-01-17   |

-
 **Note of Models:**

 The release of InternLM2 series contains two model sizes: 7B and 20B. 7B models are efficient for research and application and 20B models are more powerful and can support more complex scenarios. For each model size, there are four types of models for different user requirements
@ -75,13 +71,14 @@ The release of InternLM2 series contains two model sizes: 7B and 20B. 7B models
 1. InternLM2-Base: Foundation models with high quality and high adaptation flexibility, which serve as a good starting point for downstream deep adaptations.
 2. InternLM2: Optimized in multiple dimensions based on InternLM2-Base, obtaining state-of-the-art performance in evaluation with good language capability. InternLM2 models are recommended for consideration in most applications.
 3. InternLM2-Chat-SFT: Intermediate version of InternLM2-Chat that only undergoes supervised fine-tuning (SFT), based on the InternLM2-Base model. We release them to benefit research on alignment.
-4. InternLM2-Chat: Further aligned on top of InternLM2-Chat-SFT through online RLHF. InternLM2-Chat exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.
+4. InternLM2-Chat: Further aligned on top of InternLM2-Chat-SFT through online RLHF. InternLM2-Chat exhibits better instruction following, chat experience, and function call, which is recommended for downstream applications.

 **Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.

 ## Performance

 ### Objective Evaluation
+
 | Dataset                | Baichuan2-7B-Chat | Mistral-7B-Instruct-v0.2 | Qwen-7B-Chat | InternLM2-Chat-7B | ChatGLM3-6B | Baichuan2-13B-Chat | Mixtral-8x7B-Instruct-v0.1 | Qwen-14B-Chat | InternLM2-Chat-20B |
 |-----------------------|-------------------|--------------------------|--------------|-------------------|-------------|---------------------|--------------------------------|---------------|---------------------|
 | MMLU                  | 50.1              | 59.2                     | 57.1         | 63.7              | 58.0        | 56.6                | 70.3                          | 66.7          | 65.1                |
@ -119,7 +116,7 @@ The release of InternLM2 series contains two model sizes: 7B and 20B. 7B models
 | GPT-4 0613         | 15.76%   | 1140   |
 | Claude 2.1         | 15.73%   | 1096   |

-* According to the released performance of 2024-01-17.
+- According to the released performance of 2024-01-17.

 ## Usages

--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@ -18,12 +18,9 @@
 [![evaluation](./assets//compass_support.svg)](https://github.com/internLM/OpenCompass/)
 <!-- [![Documentation Status](https://readthedocs.org/projects/internlm/badge/?version=latest)](https://internlm.readthedocs.io/zh_CN/latest/?badge=latest) -->

-[📘对话教程](./chat) |
-[🛠️智能体教程](./agent) |
-[📊评测](#评测) |
-[👀模型库](./model_cards) |
+[📘商业授权](#开源许可证) |
 [🤗HuggingFace](https://huggingface.co/spaces/internlm/internlm2-Chat-7B) |
-[🆕Update News](#news) |
+[🆕最新消息](#更新) |
 [🤔提交反馈](https://github.com/InternLM/InternLM/issues/new)

 [English](./README.md) |
@ -39,7 +36,7 @@

 InternLM2 系列模型在本仓库正式发布，具有如下特性：

- 有效支持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。 可以通过 [LMDeploy](./inference/) 尝试20万字超长上下文推理。
+- 有效支持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。 可以通过 [LMDeploy](./chat/lmdeploy_zh_cn.md) 尝试20万字超长上下文推理。
 - 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、对话体验、指令遵循和创意写作等方面的能力提升尤为显著，综合性能达到同量级开源模型的领先水平，在重点能力评测上 InternLM2-Chat-20B 能比肩甚至超越 ChatGPT （GPT-3.5）。
 - 代码解释器与数据分析：在配合代码解释器（code-interpreter）的条件下，InternLM2-Chat-20B 在 GSM8K 和 MATH 上可以达到和 GPT-4 相仿的水平。基于在数理和工具方面强大的基础能力，InternLM2-Chat 提供了实用的数据分析能力。
 - 工具调用能力整体升级：基于更强和更具有泛化性的指令理解、工具筛选与结果反思等能力，新版模型可以更可靠地支持复杂智能体的搭建，支持对工具进行有效的多轮调用，完成较复杂的任务。可以查看更多[样例](./agent/)。
@ -79,6 +76,7 @@ InternLM2 系列模型在本仓库正式发布，具有如下特性：
 ## 性能

 ### 客观评测
+
 | Dataset                | Baichuan2-7B-Chat | Mistral-7B-Instruct-v0.2 | Qwen-7B-Chat | InternLM2-Chat-7B | ChatGLM3-6B | Baichuan2-13B-Chat | Mixtral-8x7B-Instruct-v0.1 | Qwen-14B-Chat | InternLM2-Chat-20B |
 |-----------------------|-------------------|--------------------------|--------------|-------------------|-------------|---------------------|--------------------------------|---------------|---------------------|
 | MMLU                  | 50.1              | 59.2                     | 57.1         | 63.7              | 58.0        | 56.6                | 70.3                          | 66.7          | 65.1                |
@ -116,7 +114,7 @@ InternLM2 系列模型在本仓库正式发布，具有如下特性：
 | GPT-4 0613         | 15.76%   | 1140   |
 | Claude 2.1         | 15.73%   | 1096   |

-* 性能数据截止2024-01-17
+- 性能数据截止2024-01-17

 ## 使用案例