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| title: aGLM Autonomous General Learning Model | |
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| machine learning as a process | |
| research into machine learning intelligence principals and application | |
| First iteration of <a href="https://github.com/pythaiml/automindx/blob/main/aglm.py">aglm.py</a> is available in the Professor-Codephreak LLM codebase as <a href="https://github.com/pythaiml/automindx">automindx</a> https://github.com/pythaiml/automindx | |
| as a solution to machine <a href="https://github.com/pythaiml/automindx/blob/main/memory.md">memory</a> with aglm as the memory parser | |
| aglm.py - Autonomous General Learning Model Overview | |
| The aglm.py module implements an Autonomous General Learning Model (AGLM) that utilizes a pre-trained language model to generate contextual responses based on a conversation history. It is designed to process and generate responses from conversation data stored in memory files, using a pre-trained language model. Classes and Functions LlamaModel | |
| This class represents the AGLM. It is responsible for initializing the language model and tokenizer, as well as generating contextual responses based on conversation history. | |
| __init__(self, model_name, models_folder): Constructor that initializes the AGLM with the specified model_name and models_folder. It initializes the language model and tokenizer. | |
| initialize_model(self): Initializes the language model and tokenizer using the specified model_name and models_folder. | |
| generate_contextual_output(self, conversation_context): Generates a contextual response based on the given conversation context. It formats the conversation history using format_to_llama_chat_style and generates a response using the pre-trained language model. | |
| determine_batch_size() | |
| A utility function that determines an appropriate batch size for processing memory files based on available system memory. It calculates the batch size using the total available memory and a predefined maximum memory usage threshold. main() | |
| The main entry point of the script. It reads conversation history from memory files in batches, generates a contextual response using the AGLM, and prints the response. It uses the LlamaModel class to perform response generation. Usage | |
| Import the necessary modules: os, glob, ujson, psutil, AutoModelForCausalLM, AutoTokenizer from the transformers library, and format_to_llama_chat_style from automind. | |
| Define the LlamaModel class, which encapsulates the AGLM's behavior. It initializes the language model, tokenizer, and generates responses based on conversation context. | |
| Define the utility function determine_batch_size() that calculates an appropriate batch size based on system memory. | |
| Define the main() function, which reads memory files in batches, generates responses, and prints the generated response. | |
| If the script is executed as the main program (if __name__ == '__main__':), it calls the main() function to execute the AGLM. | |
| Example Use Case | |
| The aglm.py script could be used as part of a larger system that utilizes conversation memory to generate context-aware responses in a chatbot or virtual assistant application. It reads conversation history from memory files, processes the data in batches to manage memory usage, generates responses using a pre-trained language model, and prints the generated response to the console. | |
| By integrating the aglm.py module with other components, developers can create more intelligent and contextually-aware conversational agents. |