LLM-playground

Large Language Model Playground: experiment with Llama2, Falcon, GPT, and Flan chat LLM and tune LLM parameters

Model	Source	Parameters	Fine tuned for dialog task	Open-source	Model Link
GPT	OpenAI	14.8 Billion	No	No	Click He
llama-2-70b-chat	Meta	70 Billion	Yes	Yes	Click He
llama-2-13b-chat	Meta	13 Billion	Yes	Yes	Click He
llama-2-7b-chat	Meta	7 Billion	Yes	Yes	Click He
falcon-7b-instruct	TII	7 Billion	Yes	Yes	Click He
flan-ul2	Google	20 Billion	Yes	Yes	Click He

Control the LLM powered chatbot using LLM parameters

• Six LLM models: Change the used model
• Temperature range [0.1-1]: Control the creativity of the answer
• Output length range [1-1000]: Control the output's maximum number of tokens

Features of the project

• Interacting with conversational agents via simple UI
• Fast load and delete models for assessing the LLM models' performance on specific
• API keys for all models are provided except for GPT model (provided by users)

Demo

For live demo visit https://llm-playground-etp4.onrender.com/

LLM_Demo.mp4

A Guide to Installation and Use

Make sure all libraries included in the requirements.txt file and their dependencies are installed using pip or conda command on your virtual environment.

pip install langchain==0.0.345
pip install python-dotenv==1.0.0
pip install streamlit==1.29.0
pip install replicate==0.20.0
pip install huggingface_hub==0.19.4
pip instal

https://github.com/shaimaaK/LLM-playground/assets/54285485/e18bbeaa-4b81-4f1e-84e9-2c100ff64491

l openai==1.3.5

To run the application, open the terminal in the root directory and execute the following command

streamlit run frontend.py