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@ -244,41 +244,6 @@ response = pipe(prompt)
print(response) print(response)
``` ```
### Using internLM with other AI-Agent application development frameworks
Expand the entries below for detailed usage
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).
</details>
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>
</details>
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>
Once you have installed `lazyllm`, and then you can use the following code to build your own chatbot:
```python
from lazyllm import TrainableModule, WebModule
# Model will be download automatically if you have an internet connection
m = TrainableModule('internlm2-chat-7b')
# will launch a chatbot server
WebModule(m).start().wait()
```
You can use the following code to finetune your model if needed.
```python
from lazyllm import TrainableModule, WebModule
m = TrainableModule('internlm2-chat-7b').trainset('/patt/to/your_data.json')
# TrainableModule m will be finetuned and deployed when web module is update once dataset is set
WebModule(m).update().wait()
```
LazyLLM Documents: https://lazyllm.readthedocs.io/
</details>
## Agent ## Agent
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/). 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/).