mirror of https://github.com/InternLM/InternLM
54 lines
5.3 KiB
Markdown
54 lines
5.3 KiB
Markdown
# InternLM2.5-1.8B Model Card
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## Introduction
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InternLM2.5, the 2.5th generation InternLM, has open-sourced a 1.8 billion parameter base model and a chat model tailored for practical scenarios. For the convenience of users and researchers, we have open-sourced two versions of each scale of the model, which are:
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- InternLM2.5-1.8B: 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.
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- InternLM2.5-chat-1.8B: 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.
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The model has the following characteristics:
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- **Outstanding reasoning capability**: State-of-the-art performance on Math reasoning, surpassing models like MiniCPM-2 and Qwen2-1.5B.
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- **Stronger tool use**: InternLM2.5 supports gathering information from more than 100 web pages, corresponding implementation has be released in [MindSearch](https://github.com/InternLM/MindSearch). InternLM2.5 has better tool utilization-related capabilities in instruction following, tool selection and reflection. See [examples](https://github.com/InternLM/InternLM/blob/main/agent/lagent.md).
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## Model Zoo
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| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | OpenXLab(Origin) | Release Date |
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| ------------------------- | ------------------------------------------ | ---------------------------------------- | -------------------------------------- | ------------------------------------------- | ------------ |
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| **InternLM2.5-1.8B** | [🤗internlm2_5-1_8b](https://huggingface.co/internlm/internlm2_5-1_8b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2_5-1_8b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2_5-1_8b/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-1_8b) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-1_8b-original) | 2024-08-05 |
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| **InternLM2.5-1.8B-Chat** | [🤗internlm2_5-1_8b-chat](https://huggingface.co/internlm/internlm2_5-1_8b-chat) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2_5-1_8b-chat](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2_5-1_8b-chat/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-1_8b-chat) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2_5-1_8b-chat-original) | 2024-08-05 |
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- `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).
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## Performance Evaluation
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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.
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### Base Model
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| Benchmark | InternLM2.5-1.8B | InternLM2-1.8B | Qwen2-1.5B |
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| --------- | ---------------- | -------------- | ---------- |
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| MMLU | 53.52 | 45.99 | 57.45 |
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| CMMLU | 65.44 | 45.27 | 70.58 |
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| BBH | 41.16 | 36.03 | 35.75 |
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| MATH | 27.28 | 9.42 | 24.38 |
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| HUMANEVAL | 35.98 | 30.49 | 34.15 |
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| GPQA | 24.24 | 24.24 | 31.82 |
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### Chat Model
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| Benchmark | InternLM2.5-1.8B-Chat | MiniCPM-2 | Qwen2-1.5B-Instruct |
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| ----------------- | --------------------- | --------- | ------------------- |
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| MMLU (5-shot) | 50.7 | 54.2 | 55.7 |
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| CMMLU (5-shot) | 62.2 | 50.6 | 65.2 |
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| BBH (3-shot CoT) | **41.9** | 41.5 | 36.5 |
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| MATH (0-shot CoT) | **40.2** | 15.5 | 21.4 |
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| GPQA (0-shot) | **27.8** | 23.7 | 27.3 |
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- We use `ppl` for the MCQ evaluation on base model.
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- 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).
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- 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).
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- \* means the result is copied from the original paper.
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