github
/
InternLM
mirror of https://github.com/InternLM/InternLM
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Update README.md

pull/755/head
wangzhihong 2024-07-04 19:37:09 +08:00 committed by GitHub
parent 01c1020d95
commit 5bd1777d33
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 0 additions and 35 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

35
README.md
View File

@ -244,41 +244,6 @@ response = pipe(prompt)
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
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/).