mirror of https://github.com/InternLM/InternLM
Update readme for news of InternLM-Chat-7B-v1.1 and Lagent (#213)
* update readme * fix typopull/214/head
Wenwei Zhang
GitHub
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@ -45,6 +45,10 @@ InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数
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提供了支持模型预训练的轻量级训练框架，无需安装大量依赖包，一套代码支持千卡预训练和单卡人类偏好对齐训练，同时实现了极致的性能优化，实现千卡训练下近90%加速效率。
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## 新闻
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我们开源了 InternLM-Chat-7B v1.1。该模型能够调用代码解释器和工具插件。你可以在 [Lagent](https://github.com/InternLM/lagent) 中体验这些新功能。
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## InternLM-7B
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### 性能评测
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@ -74,6 +78,7 @@ InternLM ，即书生·浦语大模型，包含面向实用场景的70亿参数
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| 模型 | InternLM 格式权重下载地址 | Transformers 格式权重下载地址 |
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| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------ |
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| **InternLM 7B** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b) | [🤗internlm/intern-7b](https://huggingface.co/internlm/internlm-7b) |
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| **InternLM Chat 7B v1.1** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-v1.1) | [🤗internlm/intern-chat-7b-v1.1](https://huggingface.co/internlm/internlm-chat-7b-v1.1) |
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| **InternLM Chat 7B** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b) | [🤗internlm/intern-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
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| **InternLM Chat 7B 8k** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-8k) | [🤗internlm/intern-chat-7b-8k](https://huggingface.co/internlm/internlm-chat-7b-8k)
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11
README.md
11
README.md
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@ -35,9 +35,6 @@
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👋 join us on <a href="https://twitter.com/intern_lm" target="_blank">Twitter</a>, <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=internwx" target="_blank">WeChat</a>
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</p>
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## Introduction
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InternLM has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics:
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@ -48,6 +45,11 @@ InternLM has open-sourced a 7 billion parameter base model and a chat model tail
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Additionally, a lightweight training framework is offered to support model pre-training without the need for extensive dependencies. With a single codebase, it supports pre-training on large-scale clusters with thousands of GPUs, and fine-tuning on a single GPU while achieving remarkable performance optimizations. InternLM achieves nearly 90% acceleration efficiency during training on 1024 GPUs.
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## News
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InternLM-7B-Chat v1.1 is released with code interpreter and function calling capability. You can try it with [Lagent](https://github.com/InternLM/lagent)
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## InternLM-7B
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### Performance Evaluation
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@ -77,6 +79,7 @@ InternLM 7B and InternLM 7B Chat, trained using InternLM, have been open-sourced
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| Model | InternLM Format Weight Download Link | Transformers Format Weight Download Link |
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| ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
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| **InternLM 7B** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b) | [🤗internlm/intern-7b](https://huggingface.co/internlm/internlm-7b) |
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| **InternLM Chat 7B v1.1** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-v1.1) | [🤗internlm/intern-chat-7b-v1.1](https://huggingface.co/internlm/internlm-chat-7b-v1.1) |
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| **InternLM Chat 7B** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b) | [🤗internlm/intern-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) |
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| **InternLM Chat 7B 8k** | [](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-8k) | [🤗internlm/intern-chat-7b-8k](https://huggingface.co/internlm/internlm-chat-7b-8k) |
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@ -136,7 +139,7 @@ We use [LMDeploy](https://github.com/InternLM/LMDeploy) to complete the one-clic
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```
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3. After exporting the model, you can start a server and have a conversation with the deployed model using the following command:
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```bash
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bash workspace/service_docker_up.sh
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python3 -m lmdeploy.serve.client {server_ip_addresss}:33337
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