mirror of https://github.com/InternLM/InternLM
add `Using internLM with other AI-Agent application development frameworks` in README.md
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README.md
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README.md
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@ -244,6 +244,41 @@ response = pipe(prompt)
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print(response)
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print(response)
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```
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```
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### Using internLM with other AI-Agent application development frameworks
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Expand the entries below for detailed usage
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1. <details><summary> <b><a href=https://github.com/langchain-ai/langchain>LangChain</a></b>: LangChain is a framework for developing applications powered by large language models (LLMs).
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</details>
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1. <details><summary> <b><a href=https://github.com/run-llama/llama_index>LlamaIndex</a></b>: LlamaIndex is a data framework for your LLM applications.</summary>
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</details>
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1. <details><summary> <b><a href=https://github.com/LazyAGI/LazyLLM>LazyLLM</a></b>: Easyest and lazyest way for building multi-agent LLMs applications.</summary>
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Once you have installed `lazyllm`, and then you can use the following code to build your own chatbot:
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```python
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from lazyllm import TrainableModule, WebModule
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# Model will be download automatically if you have an internet connection
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m = TrainableModule('internlm2-chat-7b')
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# will launch a chatbot server
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WebModule(m).start().wait()
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```
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You can use the following code to finetune your model if needed.
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```python
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from lazyllm import TrainableModule, WebModule
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m = TrainableModule('internlm2-chat-7b').trainset('/patt/to/your_data.json')
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# TrainableModule m will be finetuned and deployed when web module is update once dataset is set
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WebModule(m).update().wait()
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```
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LazyLLM Documents: https://lazyllm.readthedocs.io/
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</details>
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## Agent
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## Agent
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InternLM2.5-Chat models have excellent tool utilization capabilities and can work with function calls in a zero-shot manner. It also supports to conduct analysis by collecting information from more than 100 web pages. See more examples in [agent section](./agent/).
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InternLM2.5-Chat models have excellent tool utilization capabilities and can work with function calls in a zero-shot manner. It also supports to conduct analysis by collecting information from more than 100 web pages. See more examples in [agent section](./agent/).
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