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README.md
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README.md
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## Introduction
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InternLM2.5 series are released with the following features:
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InternLM3 has open-sourced an 8-billion parameter instruction model, InternLM3-8B-Instruct, designed for general-purpose usage and advanced reasoning. This model has the following characteristics:
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- **Outstanding reasoning capability**: State-of-the-art performance on Math reasoning, surpassing models like Llama3 and Gemma2-9B.
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- **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).
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- **Stronger tool use**: InternLM2.5 supports gathering information from more than 100 web pages, corresponding implementation will be released in [Lagent](https://github.com/InternLM/lagent/tree/main) soon. InternLM2.5 has better tool utilization-related capabilities in instruction following, tool selection and reflection. See [examples](./agent/).
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- **Enhanced performance at reduced cost**:
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State-of-the-art performance on reasoning and knowledge-intensive tasks surpass models like Llama3.1-8B and Qwen2.5-7B. Remarkably, InternLM3 is trained on only 4 trillion high-quality tokens, saving more than 75% of the training cost compared to other LLMs of similar scale.
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- **Deep thinking capability**:
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InternLM3 supports both the deep thinking mode for solving complicated reasoning tasks via the long chain-of-thought and the normal response mode for fluent user interactions.
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## News
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\[2025.01.15\] We release InternLM3-8B-Instruct, See [model zoo below](#model-zoo) for download or [model cards](./model_cards/) for more details.
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\[2024.08.01\] We release InternLM2.5-1.8B, InternLM2.5-1.8B-Chat, InternLM2.5-20B and InternLM2.5-20B-Chat. See [model zoo below](#model-zoo) for download or [model cards](./model_cards/) for more details.
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\[2024.07.19\] We release the InternLM2-Reward series of reward models in 1.8B, 7B and 20B sizes. See [model zoo below](#model-zoo) for download or [model cards](./model_cards/internlm2_reward.md) for more details.
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## Model Zoo
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### InternLM3
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| Model | Transformers(HF) | ModelScope(HF) | Modelers(HF) | Release Date |
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| ------------------------- | -------------------------------------------------------- | ------------------------------------------------------ | ----------------------------------------------------- | ------------ |
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| **InternLM3-8B-Instruct** | [🤗internlm3_8B_instruct](https://huggingface.co/internlm/internlm3-8b-instruct) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm3_8b_instruct](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct/summary) | [](https://modelers.cn/models/Intern/internlm3-8b-instruct) | 2025-01-15 |
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### InternLM2.5
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<details>
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<summary>(click to expand)</summary>
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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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**Supplements:** `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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</details>
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### InternLM2-Reward
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<details>
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<summary>(click to expand)</summary>
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InternLM2-Reward is a series of reward models, trained on 2.4 million preference samples, available in 1.8B, 7B, and 20B sizes. These model were applied to the PPO training process of our chat models. See [model cards](./model_cards/internlm2_reward.md) for more details.
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| Model | RewardBench Score | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
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| **InternLM2-7B-Reward** | 86.6 | [🤗internlm2-7b-reward](https://huggingface.co/internlm/internlm2-7b-reward) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-7b-reward](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b-reward/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b-reward) | 2024-07-19 |
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| **InternLM2-20B-Reward** | 89.5 | [🤗internlm2-20b-reward](https://huggingface.co/internlm/internlm2-20b-reward) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-20b-reward](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b-reward/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b-reward) | 2024-07-19 |
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</details>
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### InternLM2
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<details>
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## Performance
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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://rank.opencompass.org.cn) for more evaluation results.
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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://rank.opencompass.org.cn) for more evaluation results.
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### Base Model
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| Benchmark | | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(close source) |
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| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | ------------------------- |
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| General | CMMLU(0-shot) | **83.1** | 75.8 | 53.9 | 66.0 |
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| | MMLU(0-shot) | 76.6 | **76.8** | 71.8 | 82.7 |
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| | MMLU-Pro(0-shot) | **57.6** | 56.2 | 48.1 | 64.1 |
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| Reasoning | GPQA-Diamond(0-shot) | **37.4** | 33.3 | 24.2 | 42.9 |
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| | DROP(0-shot) | **83.1** | 80.4 | 81.6 | 85.2 |
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| | HellaSwag(10-shot) | **91.2** | 85.3 | 76.7 | 89.5 |
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| | KOR-Bench(0-shot) | **56.4** | 44.6 | 47.7 | 58.2 |
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| MATH | MATH-500(0-shot) | **83.0**\* | 72.4 | 48.4 | 74.0 |
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| | AIME2024(0-shot) | **20.0**\* | 16.7 | 6.7 | 13.3 |
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| Coding | LiveCodeBench(2407-2409 Pass@1) | **17.8** | 16.8 | 12.9 | 21.8 |
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| | HumanEval(Pass@1) | 82.3 | **85.4** | 72.0 | 86.6 |
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| Instrunction | IFEval(Prompt-Strict) | **79.3** | 71.7 | 75.2 | 79.7 |
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| Long Context | RULER(4-128K Average) | 87.9 | 81.4 | **88.5** | 90.7 |
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| Chat | AlpacaEval 2.0(LC WinRate) | **51.1** | 30.3 | 25.0 | 50.7 |
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| | WildBench(Raw Score) | **33.1** | 23.3 | 1.5 | 40.3 |
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| | MT-Bench-101(Score 1-10) | **8.59** | 8.49 | 8.37 | 8.87 |
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| Benchmark | InternLM2.5-7B | Llama3-8B | Yi-1.5-9B |
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| -------------- | -------------- | --------- | --------- |
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| MMLU (5-shot) | **71.6** | 66.4 | 71.6 |
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| CMMLU (5-shot) | **79.1** | 51.0 | 74.1 |
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| BBH (3-shot) | 70.1 | 59.7 | 71.1 |
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| MATH (4-shot) | **34.0** | 16.4 | 31.9 |
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| GSM8K (4-shot) | **74.8** | 54.3 | 74.5 |
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| GPQA (0-shot) | **31.3** | 31.3 | 27.8 |
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### Chat Model
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| Benchmark | InternLM2.5-7B-Chat | Llama3-8B-Instruct | Gemma2-9B-IT | Yi-1.5-9B-Chat | GLM-4-9B-Chat | Qwen2-7B-Instruct |
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| ------------------ | ------------------- | ------------------ | ------------ | -------------- | ------------- | ----------------- |
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| MMLU (5-shot) | **72.8** | 68.4 | 70.9 | 71.0 | 71.4 | 70.8 |
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| CMMLU (5-shot) | 78.0 | 53.3 | 60.3 | 74.5 | 74.5 | 80.9 |
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| BBH (3-shot CoT) | **71.6** | 54.4 | 68.2\* | 69.6 | 69.6 | 65.0 |
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| MATH (0-shot CoT) | **60.1** | 27.9 | 46.9 | 51.1 | 51.1 | 48.6 |
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| GSM8K (0-shot CoT) | 86.0 | 72.9 | 88.9 | 80.1 | 85.3 | 82.9 |
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| GPQA (0-shot) | **38.4** | 26.1 | 33.8 | 37.9 | 36.9 | 38.4 |
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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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- The evaluation results were obtained from [OpenCompass](https://github.com/internLM/OpenCompass/) (some data marked with \*, which means evaluating with Thinking Mode), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/).
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- 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/).
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**Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
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## Requirements
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## 简介
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InternLM2.5 系列模型在本仓库正式发布，具有如下特性：
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InternLM3，即书生·浦语大模型第3代，开源了80亿参数，面向通用使用与高阶推理的指令模型（InternLM3-8B-Instruct）。模型具备以下特点：
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- 卓越的推理性能：在数学推理方面取得了同量级模型最优精度，超越了 Llama3 和 Gemma2-9B。
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- 有效支持百万字超长上下文：模型在 1 百万字长输入中几乎完美地实现长文“大海捞针”，而且在 LongBench 等长文任务中的表现也达到开源模型中的领先水平。 可以通过 [LMDeploy](./chat/lmdeploy_zh_cn.md) 尝试百万字超长上下文推理。更多内容和文档对话 demo 请查看[这里](./long_context/README_zh-CN.md)。
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- 工具调用能力整体升级：InternLM2.5 支持从上百个网页搜集有效信息进行分析推理，相关实现将于近期开源到 [Lagent](https://github.com/InternLM/lagent/tree/main)。InternLM2.5 具有更强和更具有泛化性的指令理解、工具筛选与结果反思等能力，新版模型可以更可靠地支持复杂智能体的搭建，支持对工具进行有效的多轮调用，完成较复杂的任务。可以查看更多[样例](./agent/)。
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- **更低的代价取得更高的性能**:
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在推理、知识类任务上取得同量级最优性能，超过Llama3.1-8B和Qwen2.5-7B。值得关注的是InternLM3只用了4万亿词元进行训练，对比同级别模型训练成本节省75%以上。
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- **深度思考能力**:
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InternLM3支持通过长思维链求解复杂推理任务的深度思考模式，同时还兼顾了用户体验更流畅的通用回复模式。
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## 更新
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\[2025.01.15\] 我们发布了 InternLM3-8B-Instruct 模型。可以在下方的 [模型库](#model-zoo) 进行下载，或者在 [model cards](./model_cards/) 中了解更多细节。
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\[2024.08.01\] 我们发布了 InternLM2.5-1.8B、InternLM2.5-1.8B-Chat、InternLM2.5-20B 和 InternLM2.5-20B-Chat。可以在下方的 [模型库](#model-zoo) 进行下载，或者在 [model cards](./model_cards/) 中了解更多细节。
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\[2024.07.19\] 我们发布了 1.8B、7B 和 20B 大小的 InternLM2-Reward 系列奖励模型。可以在下方的 [模型库](#model-zoo) 进行下载，或者在 [model cards](./model_cards/internlm2_reward.md) 中了解更多细节。
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## Model Zoo
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### InternLM3
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| Model | Transformers(HF) | ModelScope(HF) | Modelers(HF) | Release Date |
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| ------------------------- | -------------------------------------------------------- | ------------------------------------------------------ | ----------------------------------------------------- | ------------ |
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| **InternLM3-8B-Instruct** | [🤗internlm3_8B_instruct](https://huggingface.co/internlm/internlm3-8b-instruct) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm3_8b_instruct](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct/summary) | [](https://modelers.cn/models/Intern/internlm3-8b-instruct) | 2025-01-15 |
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### InternLM2.5
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<details>
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<summary>(click to expand)</summary>
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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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**补充说明：** 上表中的 `HF` 表示对应模型为 HuggingFace 平台提供的 [transformers](https://github.com/huggingface/transformers) 框架格式；`Origin` 则表示对应模型为我们 InternLM 团队的 [InternEvo](https://github.com/InternLM/InternEvo) 框架格式。
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</details>
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### InternLM2-Reward
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<details>
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<summary>(click to expand)</summary>
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InternLM2-Reward 是基于 240 万个偏好样本进行训练的奖励模型，有 1.8B、7B 和 20B 大小可供选择。这些模型被用于 InternLM 对话模型的 PPO 训练过程。请参考 [model cards](./model_cards/internlm2_reward.md) 了解更多细节。
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| Model | RewardBench Score | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
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| **InternLM2-7B-Reward** | 86.6 | [🤗internlm2-7b-reward](https://huggingface.co/internlm/internlm2-7b-reward) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-7b-reward](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b-reward/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b-reward) | 2024-07-19 |
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| **InternLM2-20B-Reward** | 89.5 | [🤗internlm2-20b-reward](https://huggingface.co/internlm/internlm2-20b-reward) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-20b-reward](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b-reward/summary) | [](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b-reward) | 2024-07-19 |
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</details>
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### InternLM2
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<details>
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@ -126,34 +145,31 @@ InternLM2-Reward 是基于 240 万个偏好样本进行训练的奖励模型，
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## 性能
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我们使用开源评测工具 [OpenCompass](https://github.com/open-compass/opencompass) 在几个重要的基准测试中对 InternLM2.5 进行了评测。部分评测结果如下表所示。欢迎访问 [OpenCompass 排行榜](https://rank.opencompass.org.cn) 获取更多评测结果。
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我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://rank.opencompass.org.cn)获取更多的评测结果。
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### 基座模型
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| 评测集\\模型 | | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(close source) |
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| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | ------------------------- |
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| General | CMMLU(0-shot) | **83.1** | 75.8 | 53.9 | 66.0 |
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| | MMLU(0-shot) | 76.6 | **76.8** | 71.8 | 82.7 |
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| | MMLU-Pro(0-shot) | **57.6** | 56.2 | 48.1 | 64.1 |
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| Reasoning | GPQA-Diamond(0-shot) | **37.4** | 33.3 | 24.2 | 42.9 |
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| | DROP(0-shot) | **83.1** | 80.4 | 81.6 | 85.2 |
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| | HellaSwag(10-shot) | **91.2** | 85.3 | 76.7 | 89.5 |
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| | KOR-Bench(0-shot) | **56.4** | 44.6 | 47.7 | 58.2 |
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| MATH | MATH-500(0-shot) | **83.0**\* | 72.4 | 48.4 | 74.0 |
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| | AIME2024(0-shot) | **20.0**\* | 16.7 | 6.7 | 13.3 |
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| Coding | LiveCodeBench(2407-2409 Pass@1) | **17.8** | 16.8 | 12.9 | 21.8 |
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| | HumanEval(Pass@1) | 82.3 | **85.4** | 72.0 | 86.6 |
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| Instrunction | IFEval(Prompt-Strict) | **79.3** | 71.7 | 75.2 | 79.7 |
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| LongContext | RULER(4-128K Average) | 87.9 | 81.4 | **88.5** | 90.7 |
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| Chat | AlpacaEval 2.0(LC WinRate) | **51.1** | 30.3 | 25.0 | 50.7 |
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| | WildBench(Raw Score) | **33.1** | 23.3 | 1.5 | 40.3 |
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| | MT-Bench-101(Score 1-10) | **8.59** | 8.49 | 8.37 | 8.87 |
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| Benchmark | InternLM2.5-7B | Llama3-8B | Yi-1.5-9B |
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| -------------- | -------------- | --------- | --------- |
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| MMLU (5-shot) | **71.6** | 66.4 | 71.6 |
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| CMMLU (5-shot) | **79.1** | 51.0 | 74.1 |
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| BBH (3-shot) | 70.1 | 59.7 | 71.1 |
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| MATH (4-shot) | **34.0** | 16.4 | 31.9 |
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| GSM8K (4-shot) | **74.8** | 54.3 | 74.5 |
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| GPQA (0-shot) | **31.3** | 31.3 | 27.8 |
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- 以上评测结果基于 [OpenCompass](https://github.com/internLM/OpenCompass/) 获得(部分数据标注`*`代表使用深度思考模式进行评测)，具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。
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- 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。
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|
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### 对话模型
|
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|
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| Benchmark | InternLM2.5-7B-Chat | Llama3-8B-Instruct | Gemma2-9B-IT | Yi-1.5-9B-Chat | GLM-4-9B-Chat | Qwen2-7B-Instruct |
|
||||
| ------------------ | ------------------- | ------------------ | ------------ | -------------- | ------------- | ----------------- |
|
||||
| MMLU (5-shot) | **72.8** | 68.4 | 70.9 | 71.0 | 71.4 | 70.8 |
|
||||
| CMMLU (5-shot) | 78.0 | 53.3 | 60.3 | 74.5 | 74.5 | 80.9 |
|
||||
| BBH (3-shot CoT) | **71.6** | 54.4 | 68.2\* | 69.6 | 69.6 | 65.0 |
|
||||
| MATH (0-shot CoT) | **60.1** | 27.9 | 46.9 | 51.1 | 51.1 | 48.6 |
|
||||
| GSM8K (0-shot CoT) | 86.0 | 72.9 | 88.9 | 80.1 | 85.3 | 82.9 |
|
||||
| GPQA (0-shot) | **38.4** | 26.1 | 33.8 | 37.9 | 36.9 | 38.4 |
|
||||
|
||||
- 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。
|
||||
- 由于 [OpenCompass](https://github.com/open-compass/opencompass) 的版本迭代，评测数据可能存在数值差异，因此请参考 [OpenCompass](https://github.com/open-compass/opencompass) 的最新评测结果。
|
||||
- \* 表示从原论文中复制而来。
|
||||
**局限性：** 尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。
|
||||
|
||||
## 依赖
|
||||
|
||||
|
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|
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Loading…
Reference in New Issue