2024-01-17 01:46:11 +00:00
# InternLM-7B Model Card
## Introduction
InternLM-7B contains a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics:
- It leverages trillions of high-quality tokens for training to establish a powerful knowledge base.
- It supports an 8k context window length, enabling longer input sequences and stronger reasoning capabilities.
- It provides a versatile toolset for users to flexibly build their own workflows.
## Model Zoo
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | OpenXLab(Original) | Release Date |
|---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
2024-01-17 04:24:08 +00:00
| **InternLM Chat 7B** | [🤗internlm/internlm-chat-7b ](https://huggingface.co/internlm/internlm-chat-7b ) | [<img src="../assets/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-chat-7b ](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-chat-7b/summary ) | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b) | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-original) | 2023-12-12 |
| **InternLM 7B** | [🤗internlm/internlm-7b ](https://huggingface.co/internlm/internlm-7b ) | [<img src="../assets/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-7b ](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-7b/summary ) | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b) | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b-original) | 2023-07-06 |
2024-01-17 01:46:11 +00:00
## Performance Evaluation
We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass ](https://github.com/internLM/OpenCompass/ ). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard ](https://opencompass.org.cn/rank ) for more evaluation results.
| Datasets\Models | **InternLM-Chat-7B** | **InternLM-7B** | LLaMA-7B | Baichuan-7B | ChatGLM2-6B | Alpaca-7B | Vicuna-7B |
| --------------- | -------------------------- | --------------------- | -------- | ----------- | ----------- | --------- | --------- |
| C-Eval(Val) | 52.0 | 53.4 | 24.2 | 42.7 | 50.9 | 28.9 | 31.2 |
| MMLU | 52.6 | 51.0 | 35.2* | 41.5 | 46.0 | 39.7 | 47.3 |
| AGIEval | 46.4 | 37.6 | 20.8 | 24.6 | 39.0 | 24.1 | 26.4 |
| CommonSenseQA | 80.8 | 59.5 | 65.0 | 58.8 | 60.0 | 68.7 | 66.7 |
| BUSTM | 80.6 | 50.6 | 48.5 | 51.3 | 55.0 | 48.8 | 62.5 |
| CLUEWSC | 81.8 | 59.1 | 50.3 | 52.8 | 59.8 | 50.3 | 52.2 |
| MATH | 5.0 | 7.1 | 2.8 | 3.0 | 6.6 | 2.2 | 2.8 |
| GSM8K | 36.2 | 31.2 | 10.1 | 9.7 | 29.2 | 6.0 | 15.3 |
| HumanEval | 15.9 | 10.4 | 14.0 | 9.2 | 9.2 | 9.2 | 11.0 |
| RACE(High) | 80.3 | 57.4 | 46.9* | 28.1 | 66.3 | 40.7 | 54.0 |
- The evaluation results were obtained from [OpenCompass 20230706 ](https://github.com/internLM/OpenCompass/ ) (some data marked with *, which means come from the original papers), and evaluation configuration can be found in the configuration files provided by [OpenCompass ](https://github.com/internLM/OpenCompass/ ).
- The evaluation data may have numerical differences due to the version iteration of [OpenCompass ](https://github.com/internLM/OpenCompass/ ), so please refer to the latest evaluation results of [OpenCompass ](https://github.com/internLM/OpenCompass/ ).
