# InternLM
 
InternLM HOT
 
[![license](./assets/license.svg)](./LICENSE) [![evaluation](./assets/compass_support.svg)](https://github.com/internLM/OpenCompass/) [📘Chat](./chat) | [🛠️Agent](./agent) | [📊Evaluation](./evaluation) | [👀Model](./model_cards) | [🤗HuggingFace](https://huggingface.co/spaces/internlm/internlm2-Chat-7B) | [🆕Update News](#news) | [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new) [English](./README.md) | [简体中文](./README_zh-CN.md) |

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## Introduction - **200K Context window**: Both base and chat models can work with more than 200K context after being sufficiently trained on 32K-context data. Try it with [LMDeploy](./inference/) for 200K-context inference. - **Outstanding comprehensive performance**: Significantly better than the last generation in all dimensions including reasoning, code, chat experience, instruction following, and creative writing. - **Code interpreter & Data analysis**: New state-of-the-art results in using code interpreter for math problems, also good at data analysis. - **Stronger tool use**: Excellent zero-shot and multi-step tool calling capabilities, better with [streaming](docs/chat_format.md##streaming-style) and also works with [ReAct](docs/chat_format.md##react-style) format. Try it with [Lagent](./agent/). ## News [2024.01.17] We release InternLM2-7B and InternLM2-20B and their corresponding chat models with stronger capabilities in all dimensions. See [model zoo below](#model-zoo) for download or [model cards](./model_cards/) for more details. [2023.12.13] InternLM-7B-Chat and InternLM-20B-Chat checkpoints are updated. With an improved finetuning strategy, the new chat models can generate higher quality responses with greater stylistic diversity. [2023.09.20] InternLM-20B is released with base and chat versions. ## Model Zoo | Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date | |---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------| | **InternLM2 Chat 20B** | [🤗internlm/internlm-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b) | [ internlm2-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b) | 2024-01-17 | | **InternLM2 20B** | [🤗internlm/internlm2-20b](https://huggingface.co/internlm/internlm2-20b) | [ internlm2-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b) | 2024-01-17 | | **InternLM2 Chat 20B SFT** | [🤗internlm/internlm-chat-20b-sft](https://huggingface.co/internlm/internlm2-chat-20b-sft) | [ internlm2-chat-20b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b-sft) | 2024-01-17 | | **InternLM2 Base 20B** | [🤗internlm/internlm2-base-20b](https://huggingface.co/internlm/internlm2-base-20b) | [ internlm2-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-20b) | 2024-01-17 | | **InternLM2 Chat 7B** | [🤗internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) | [ internlm2-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b) | 2024-01-17 | | **InternLM2 7B** | [🤗internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b) | [ internlm2-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b) | 2024-01-17 | | **InternLM2 Chat 7B SFT** | [🤗internlm/internlm2-chat-7b-sft](https://huggingface.co/internlm/internlm2-chat-7b-sft) | [ internlm2-chat-7b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b-sft) | 2024-01-17 | | **InternLM2 Base 7B** | [🤗internlm/internlm2-base-7b](https://huggingface.co/internlm/internlm2-base-7b) | [ internlm2-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-7b) | 2024-01-17 | **Note:** 1. For chat models, InternLM2 Chat 7/20B has gone through online RLHF for better alignment, which is recommended for downstream applications. We also released InternLM2 Chat 7/20B SFT, which are the ones that only have gone through SFT and used in RLHF to obtain InternLM2 Chat 7/20B. InternLM2 Chat 7/20B are trained from InternLM2 Base 7/20B. 2. For base models, InternLM2 7/20B are further trained from InternLM2 Base 7/20B, which is recommended for fast adaptation for downstream applications. **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. ## Usages We briefly show the usages with [Transformers](#import-from-transformers), [ModelScope](#import-from-modelscope), and [Web demos](#dialogue). The chat models adopt [chatml format](./chat/chat_format.md) to support both chat and agent applications. ### Import from Transformers To load the InternLM2 7B Chat model using Transformers, use the following code: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True) >>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True).cuda() >>> model = model.eval() >>> response, history = model.chat(tokenizer, "hello", history=[]) >>> print(response) Hello! How can I help you today? >>> response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history) >>> print(response) ``` ### Import from ModelScope To load the InternLM model using ModelScope, use the following code: ```python from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM import torch model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm2-chat-7b') tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16) model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16) model = model.eval() response, history = model.chat(tokenizer, "hello", history=[]) print(response) response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history) print(response) ``` ### Dialogue You can interact with the InternLM Chat 7B model through a frontend interface by running the following code: ```bash pip install streamlit==1.24.0 pip install transformers==4.30.2 streamlit run ./chat/web_demo.py ``` The effect is similar to below: ![demo](https://github.com/InternLM/InternLM/assets/9102141/11b60ee0-47e4-42c0-8278-3051b2f17fe4) ### Deployment We use [LMDeploy](https://github.com/InternLM/LMDeploy) for fast deployment of InternLM. ```shell # install LMDeploy python3 -m pip install lmdeploy # chat with internlm2 lmdeploy chat turbomind InternLM/internlm2-chat-7b --model-name internlm2-chat-7b ``` Please refer to the [guidance](./chat/lmdeploy.md) for more usages about model deployment. For additional deployment tutorials, feel free to explore [here](https://github.com/InternLM/LMDeploy). ## Agent InternLM2-Chat models have excellent tool utilization capabilities and can work with function calls in a zero-shot manner. See more examples in [agent session](./agent/). ## Fine-tuning Please refer to [finetune docs](./finetune/) for fine-tuning with InternLM. **Note:** We have migrated the whole training functionality in this project to [InternEvo](https://github.com/InternLM/InternEvo) for easier user experience, which provides efficient pre-training and fine-tuning infra for training InternLM. ## Contribution We appreciate all the contributors for their efforts to improve and enhance InternLM. Community users are highly encouraged to participate in the project. Please refer to the contribution guidelines for instructions on how to contribute to the project. ## License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage. To apply for a commercial license, please fill in the [application form (English)](https://wj.qq.com/s2/12727483/5dba/)/[申请表（中文）](https://wj.qq.com/s2/12725412/f7c1/). For other questions or collaborations, please contact . ## Citation ``` @misc{2023internlm, title={InternLM: A Multilingual Language Model with Progressively Enhanced Capabilities}, author={InternLM Team}, howpublished = {\url{https://github.com/InternLM/InternLM}}, year={2023} } ```