Imports and Definitions:
The code begins by importing necessary libraries and modules, including Gradio, fire, and others.
The Model_Type enumeration is defined to categorize different types of models.
Model Type Detection (get_model_type function):
The get_model_type function takes a model name and returns its type based on keywords.
It categorizes models as gptq, ggml, or full_precision.
Folder Creation Function (create_folder_if_not_exists function):
This function creates a folder at the specified path if it doesn't exist.
Model Initialization and Tokenization (initialize_gpu_model_and_tokenizer function):
This function initializes the GPU-based model and tokenizer based on the model type.
If the model type is gptq, it initializes an AutoGPTQForCausalLM model and tokenizer.
Otherwise, it initializes an AutoModelForCausalLM model and tokenizer.
Automatic Model and Tokenizer Initialization (init_auto_model_and_tokenizer function):
This function initializes the appropriate model and tokenizer based on the model type.
If the model type is ggml, it downloads the model from Hugging Face Hub and initializes a Llama model.
For other model types, it calls initialize_gpu_model_and_tokenizer.
User Interaction UI (run_ui function):
This function defines the UI components using Gradio.
It defines a chatbot textbox, user message textbox, and a clear button.
It handles user input and maintains conversation history in conversation_memory.
User Input Handling (user function):
This function updates conversation memory with user messages.
Bot Response Generation (bot function):
This function generates a bot response based on conversation history and user input.
For ggml models, it generates responses using the Llama model.
For other models, it generates responses using a GPU-based model and tokenizer.
UI Configuration and Launch:
The gr.Interface is used to define the UI with input components and interactions.
The UI is launched using .launch.
Main Function (main function):
The main function is used to handle command-line arguments and initiate UI.
It determines if the model is chat-based and loads appropriate models and tokenizers.
It then calls run_ui to launch the user interface.
Script Execution (__name__ == '__main__'):
The script is executed when run directly (not imported as a module).
The fire.Fire(main) line uses the fire library to handle command-line arguments and execute the main function.
Optional Response Generation from Memory:
After launching the UI, the script attempts to generate a response from memory.
uiux.py uses the generate_response_from_memory aglm.py function.
In summary, the uiux.py code provides a user interface for interacting with different models using Gradio. It initializes the appropriate models and tokenizers, manages user inputs, generates bot responses, and maintains conversation history. The UI is configured and launched using uiux.py components and features.
python uiux.py --model_name="TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML" --tokenizer_name="TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML" --model_type="ggml" --save_history --file_name="llama-2-7b-chat-codeCherryPop.ggmlv3.q4_1.bin"