mirror of https://github.com/InternLM/InternLM
40 lines
5.4 KiB
Markdown
40 lines
5.4 KiB
Markdown
# InternLM2-1.8B Model Card
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## Introduction
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InternLM2-1.8B is the 1.8 billion parameter version of the second generation InternLM series. In order to facilitate user use and research, InternLM2-1.8B has three versions of open-source models. They are:
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- InternLM2-1.8B: Foundation models with high quality and high adaptation flexibility, which serve as a good starting point for downstream deep adaptations.
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- InternLM2-Chat-1.8B-SFT: Chat model after supervised fine-tuning (SFT) on InternLM2-1.8B.
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- InternLM2-Chat-1.8B: Further aligned on top of InternLM2-Chat-1.8B-SFT through online RLHF. InternLM2-Chat-1.8B exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.
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The base model of InternLM2 has the following technical features:
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- Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval.
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- Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.
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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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| **InternLM2-1.8B** | [🤗internlm2-1.8b](https://huggingface.co/internlm/internlm2-1_8b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-1.8b](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-1_8b/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-1.8b) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-1.8b-original) | 2024-01-31 |
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| **InternLM2-Chat-1.8B-SFT** | [🤗internlm2-chat-1.8b-sft](https://huggingface.co/internlm/internlm2-chat-1_8b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-1.8b-sft](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-1_8b-sft/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-1.8b-sft) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-1.8b-sft-original) | 2024-01-31 |
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| **InternLM2-Chat-1.8B** | [🤗internlm2-chat-1.8b](https://huggingface.co/internlm/internlm2-chat-1_8b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-1.8b](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-1_8b/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-1.8b) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-1.8b-original) | 2024-02-19 |
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## Performance Evaluation
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We have evaluated InternLM2 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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| Dataset\\Models | InternLM2-1.8B | InternLM2-Chat-1.8B-SFT | InternLM2-Chat-1.8B | InternLM2-7B | InternLM2-Chat-7B |
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| :-------------: | :------------: | :---------------------: | :-----------------: | :----------: | :---------------: |
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| MMLU | 46.9 | 47.1 | 44.1 | 65.8 | 63.7 |
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| AGIEval | 33.4 | 38.8 | 34.6 | 49.9 | 47.2 |
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| BBH | 37.5 | 35.2 | 34.3 | 65.0 | 61.2 |
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| GSM8K | 31.2 | 39.7 | 34.3 | 70.8 | 70.7 |
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| MATH | 5.6 | 11.8 | 10.7 | 20.2 | 23.0 |
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| HumanEval | 25.0 | 32.9 | 29.3 | 43.3 | 59.8 |
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| MBPP(Sanitized) | 22.2 | 23.2 | 27.0 | 51.8 | 51.4 |
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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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