LLM Pandoras

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LLM-Workbench is a toolkit for training, fine-tuning, and visualizing language models using Streamlit. It is very suitable for researchers and AI enthusiasts.

Features

• ? Knowledge Base Q&A: We provide various ways of knowledge retrieval, where es uses 8.9.0 hybrid search to search for relevant knowledge fragments based on input and answer.
• Excel Q&A: We use chatglm3 to generate corresponding python code based on the question, and use the kernel to execute the relevant code to return the result.
• ? Model Training: With our toolkit, you can easily train your own language model.
• ? Model Fine-tuning: We provide a simple way to fine-tune your model to better adapt to your specific task.
• Model Visualization: Our toolkit includes some visualization tools that can help you better understand your model.

? Docker-compose Installation

Install ElasticSearch (please open the corresponding server port according to the docker-compose file, or customize it):

cd docker/es
docker-compose up -d

To use the knowledge base Q&A, you need to build the corresponding index:

Method one: You can install LLM-Workbench with the following command:

cd LLM-Workbench
docker-compose up -d

For the case of using excel table Q&A, you need to enter the container and specify the kernel interpreter:

ipython kernel install --name llm --user

Where, llm corresponds to the conda environment name.

? Usage

Method two: After installing LLM-Workbench, you can start it with the following command:

pip install -r requirements.txt
streamlit run chat-box.py

Then, you can open the displayed URL in your browser to start using LLM-Workbench.

? Contribution

We welcome any form of contribution! If you have any questions or suggestions, feel free to raise them on GitHub.

? License

LLM-Workbench is released under the MIT license. For more details, please see the LICENSE file.

? Contact Us

If you have any questions or suggestions, feel free to ask us via email or GitHub.

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