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RangiLyu 2024-07-03 20:27:12 +08:00
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README_zh-CN.md
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@ -120,14 +120,14 @@ InternLM2.5 系列模型在本仓库正式发布，具有如下特性：
### 对话模型
| Benchmark | InternLM2-Chat-7B | LLaMA-3-8B-Instruct | Yi-1.5-9B-Chat | GLM-4-9B-Chat | Qwen2-7B-Instruct | Gemma2-9B-IT | InternLM2.5-7B-Chat |
| ----------------- | ----------------- | ------------------- | -------------- | ------------- | ----------------- | ------------ | ------------------- |
| MMLU(5-shot) | 62.3 | 68.4 | 71.0 | 71.4 | 70.8 | 70.9 | 72.8 |
| CMMLU(5-shot) | 62.4 | 53.3 | 74.5 | 74.5 | 80.9 | 60.3 | 78.0 |
| BBH(3-shot CoT) | 59.0 | 54.4 | 69.6 | 69.6 | 65.0 | 68.2\* | 71.6 |
| MATH(0-shot CoT) | 27.6 | 27.9 | 51.1 | 51.1 | 48.6 | 46.9 | 60.1 |
| GSM8K(0-shot CoT) | 72.5 | 72.9 | 80.1 | 85.3 | 82.9 | 88.9 | 86.0 |
| GPQA(0-shot) | 29.8 | 26.1 | 37.9 | 36.9 | 38.4 | 33.8 | 38.4 |
| Benchmark | InternLM2-Chat-7B | LLaMA-3-8B-Instruct | Yi-1.5-9B-Chat | GLM-4-9B-Chat | Qwen2-7B-Instruct | Gemma2-9B-IT | InternLM2.5-7B-Chat | Llama-3-70B-Instruct |
| ----------------- | ----------------- | ------------------- | -------------- | ------------- | ----------------- | ------------ | ------------------- | -------------------- |
| MMLU(5-shot) | 62.3 | 68.4 | 71.0 | 71.4 | 70.8 | 70.9 | 72.8 | 80.5 |
| CMMLU(5-shot) | 62.4 | 53.3 | 74.5 | 74.5 | 80.9 | 60.3 | 78.0 | 70.1 |
| BBH(3-shot CoT) | 59.0 | 54.4 | 69.6 | 69.6 | 65.0 | 68.2\* | 71.6 | 80.5 |
| MATH(0-shot CoT) | 27.6 | 27.9 | 51.1 | 51.1 | 48.6 | 46.9 | 60.1 | 47.1 |
| GSM8K(0-shot CoT) | 72.5 | 72.9 | 80.1 | 85.3 | 82.9 | 88.9 | 86.0 | 92.8 |
| GPQA(0-shot) | 29.8 | 26.1 | 37.9 | 36.9 | 38.4 | 33.8 | 38.4 | 38.9 |
- 我们使用 `ppl` 对基座模型进行 MCQ 指标的评测。
- 评测结果来自 [OpenCompass](https://github.com/open-compass/opencompass) ，评测配置可以在 [OpenCompass](https://github.com/open-compass/opencompass) 提供的配置文件中找到。

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model_cards/internlm2.5_7b.md
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@ -11,7 +11,7 @@ InternLM2.5, the 2.5th generation InternLM, has open-sourced a 7 billion paramet
The model has the following characteristics:
- **Outstanding reasoning capability**: State-of-the-art performance on Math reasoning, surpassing models like Llama3 and Gemma2-9B.
- **1M Context window**: Nearly perfect at finding needles in the haystack with 1M-long context, with leading performance on long-context tasks like LongBench. Try it with LMDeploy for 1M-context inference.
- **1M Context window**: Nearly perfect at finding needles in the haystack with 1M-long context, with leading performance on long-context tasks like LongBench. Try it with [LMDeploy](./chat/lmdeploy.md) for 1M-context inference. More details and a file chat demo are found [here](./long_context/README.md).
- **Stronger tool use**: InternLM2.5 supports gathering information from more than 100 web pages, corresponding implementation will be released in Lagent soon. InternLM2.5 has better tool utilization-related capabilities in instruction following, tool selection and reflection. See [examples](https://huggingface.co/internlm/internlm2_5-7b-chat-1m/blob/main/agent/).
## Model Zoo