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Merge branch 'update_link_and_reward' into 'main' 

update opencompass link and reward readme

See merge request openmmlab/bigmodel/InternLM!15
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lvchengqi 2024-07-02 08:34:00 +00:00
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README.md
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@ -84,9 +84,6 @@ The release of InternLM2.5 series contains 7B model size for now and we are goin
### InternLM-Reward
<details>
<summary>(click to expand)</summary>
InternLM-Reward is a series of reward models, trained on 2.4 million preference samples, available in 1.8B, 7B, and 20B sizes. These model were applied to the PPO training process of our chat models. See [model cards](./model_cards/internlm_reward.md) for more details.
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
@ -95,8 +92,6 @@ InternLM-Reward is a series of reward models, trained on 2.4 million preference
| **InternLM-Reward-7B** | [🤗internlm-reward-7b](https://huggingface.co/internlm/internlm-reward-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm-reward-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-reward-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm-reward-7b) | 2024-06-30 |
| **InternLM-Reward-20B** | [🤗internlm-reward-20b](https://huggingface.co/internlm/internlm-reward-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm-reward-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-reward-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm-reward-20b) | 2024-06-30 |
</details>
### InternLM2
<details>
@ -122,7 +117,7 @@ Our previous generation models with advanced capabilities in long-context proces
## Performance
We have evaluated InternLM2.5 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.
We have evaluated InternLM2.5 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://rank.opencompass.org.cn) for more evaluation results.
### Base Model

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README_zh-CN.md
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@ -70,9 +70,7 @@ InternLM2.5 系列模型在本仓库正式发布，具有如下特性：
**模型说明：**
目前 InternLM 2.5 系列只发布了 7B 大小的模型，我们接下来将开源 1.8B 和 20B 的版本。7B 为轻量级的研究和应用提供了一个轻便但性能不俗的模型，20B 模型的综合性能更为强劲，可以有效支持更加复杂的实用场景。每个规格不同模型关系如下图所示：
![](https://internlm.oss-cn-shanghai.aliyuncs.com/series.png)
目前 InternLM 2.5 系列只发布了 7B 大小的模型，我们接下来将开源 1.8B 和 20B 的版本。7B 为轻量级的研究和应用提供了一个轻便但性能不俗的模型，20B 模型的综合性能更为强劲，可以有效支持更加复杂的实用场景。每个规格不同模型关系如下所示：
1. **InternLM2.5**：经历了大规模预训练的基座模型，是我们推荐的在大部分应用中考虑选用的优秀基座。
2. **InternLM2.5-Chat**: 对话模型，在 InternLM2.5 基座上经历了有监督微调和 online RLHF。InternLM2。5-Chat 面向对话交互进行了优化，具有较好的指令遵循、共情聊天和调用工具等的能力，是我们推荐直接用于下游应用的模型。
@ -84,9 +82,6 @@ InternLM2.5 系列模型在本仓库正式发布，具有如下特性：
### InternLM-Reward
<details>
<summary>(click to expand)</summary>
InternLM-Reward 是基于 240 万个偏好样本进行训练的奖励模型，有 1.8B、7B 和 20B 大小可供选择。这些模型被用于 InternLM 对话模型的 PPO 训练过程。请参考 [model cards](./model_cards/internlm_reward.md) 了解更多细节。
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
@ -95,8 +90,6 @@ InternLM-Reward 是基于 240 万个偏好样本进行训练的奖励模型，
| **InternLM-Reward-7B** | [🤗internlm-reward-7b](https://huggingface.co/internlm/internlm-reward-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm-reward-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-reward-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm-reward-7b) | 2024-06-30 |
| **InternLM-Reward-20B** | [🤗internlm-reward-20b](https://huggingface.co/internlm/internlm-reward-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm-reward-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-reward-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm-reward-20b) | 2024-06-30 |
</details>
### InternLM2
<details>
@ -122,7 +115,7 @@ InternLM-Reward 是基于 240 万个偏好样本进行训练的奖励模型，
## 性能
我们使用开源评测工具 [OpenCompass](https://github.com/open-compass/opencompass) 在几个重要的基准测试中对 InternLM2.5 进行了评测。部分评测结果如下表所示。欢迎访问 [OpenCompass 排行榜](https://opencompass.org.cn/rank) 获取更多评测结果。
我们使用开源评测工具 [OpenCompass](https://github.com/open-compass/opencompass) 在几个重要的基准测试中对 InternLM2.5 进行了评测。部分评测结果如下表所示。欢迎访问 [OpenCompass 排行榜](https://rank.opencompass.org.cn) 获取更多评测结果。
### 基座模型