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[Docs] add internlm2.5 model card

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# InternLM2.5-7B Model Card
## Introduction
InternLM2.5, the 2.5th generation InternLM, has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. For the convenience of users and researchers, we have open-sourced three versions of each scale of the model, which are:
- InternLM2.5-7B: Further pretrain with general domain data and domain-enhanced corpus, obtaining state-of-the-art performance in evaluation with good language capability. InternLM2.5 models are recommended for consideration in most applications.
- InternLM2.5-chat-7B: Further aligned on top of InternLM2.5 through supervised fine-tuning (SFT) and online RLHF. InternLM2.5-Chat exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.
- InternLM2.5-7B-Chat-1M: 1M-long-context version of InternLM2.5-7B-Chat. InternLM2.5-Chat-1M supports million-word extra-long contextual reasoning while maintaining the same performance as InternLM2.5-Chat.
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.
- **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
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | OpenXLab(Origin) | Release Date |
| ------------------------- | ------------------------------------------ | ---------------------------------------- | -------------------------------------- | ------------------------------------------- | ------------ |
| **InternLM2.5-7B** | [🤗internlm2_5-7b](https://huggingface.co/internlm/internlm2_5-7b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2_5-7b](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm2_5-7b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-7b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-7b-original) | 2024-07-01 |
| **InternLM2.5-chat-7B** | [🤗internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-7b-chat) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2_5-7b-chat](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2_5-7b-chat) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-7b-chat) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-7b-chat-original) | 2024-07-01 |
| **InternLM2.5-7B-Chat-1M** | [🤗internlm2_5-7b-chat-1m](https://huggingface.co/internlm/internlm2_5-7b-chat-1m) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2_5-7b-chat-1m](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-7b-chat-1m) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-7b-chat-1m-original) | 2024-07-01 |
- `HF` refers to the format used by HuggingFace in [transformers](https://github.com/huggingface/transformers), whereas `Origin` denotes the format adopted by the InternLM team in [InternEvo](https://github.com/InternLM/InternEvo).
## Performance Evaluation
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.
| Benchmark | InternLM2.5-7B | InternLM2-7B | LLaMA3-8B | Yi-1.5-9B |
|-----------|----------------|--------------|-----------|-----------|
| MMLU | 71.6 | 65.8 | 66.4 | 71.6 |
| CMMLU | 79.1 | 66.2 | 51.0 | 74.1 |
| BBH | 70.1 | 65.0 | 59.7 | 71.1 |
| MATH | 34.0 | 20.2 | 16.4 | 31.9 |
| GSM8K | 74.8 | 70.8 | 54.3 | 74.5 |
| GPQA | 31.3 | 28.3 | 31.3 | 27.8 |
- The evaluation results were obtained from [OpenCompass](https://github.com/open-compass/opencompass) , and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/open-compass/opencompass).
- The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/open-compass/opencompass), so please refer to the latest evaluation results of [OpenCompass](https://github.com/open-compass/opencompass).