Update README.md

README.md

@@ -22,29 +22,34 @@ This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unsloth
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
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+## How the MOE System Works
 
-A Multi-Expert Question Answering System
----
+This model is a core component of a larger Multi-Expert Question Answering System. Here's a breakdown of the system's functionality:
 
-This Python program implements a simple multi-expert question answering system using a Mixture of Experts (MOE) approach with large language models (LLMs).
+1. **Model Loading:** The system loads the "director" LLM and keeps other expert LLMs (e.g., for programming, biology, mathematics) ready for use.
+2. **Expert Routing:** When a user asks a question, the system either:
+   - Uses keyword matching to identify the relevant domain.
+   - Consults the director LLM to classify the question's category.
+3. **Dynamic Expert Loading:** The system loads the chosen expert LLM into memory, optimizing resource usage by releasing any previously active expert.
+4. **Response Generation:** The selected expert LLM receives the question and generates a tailored answer.
+5. **Chat Interface:** A user-friendly chat interface facilitates interaction with the MOE system.
 
-Here's how it works: Model Loading: It loads a "director" LLM and keeps expert LLMs for programming, biology, and mathematics on standby. Expert Routing: When a user asks a question, the program uses keyword matching or consults the director LLM to determine the most relevant expert. Dynamic Expert Loading: It loads the chosen expert LLM into memory, releasing the previous expert to conserve resources. Response Generation: The program prompts the selected expert LLM with the question and returns the generated answer to the user. Chat Interface: A simple chat interface allows users to interact with the system and ask questions on various topics. This MOE approach allows for more efficient and potentially more accurate responses compared to using a single general-purpose LLM..
+This MOE approach enhances efficiency and accuracy compared to relying on a single, general-purpose LLM.
 
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----
+Repository and Additional Information
+Full Code: https://huggingface.co/Agnuxo/Qwen2-1.5B-Instruct_MOE_Director_16bit/resolve/main/MOE-LLMs3.py
+GitHub Repository: https://github.com/Agnuxo1/NEBULA
 
-https://huggingface.co/Agnuxo/Qwen2-1.5B-Instruct_MOE_Director_16bit/resolve/main/MOE-LLMs3.py 
----
 
----
+## Code Example
+
+The following code demonstrates the implementation of the Multi-Expert Question Answering System:
+
+```python
 import os
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
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-
-# Model configuration
 MODEL_CONFIG = {
     "director": {
         "name": "Agnuxo/Qwen2-1.5B-Instruct_MOE_Director_16bit",
@@ -64,18 +69,13 @@ MODEL_CONFIG = {
     }
 }
 
----
----
-# Keywords for each subject
+
 KEYWORDS = {
     "biology": ["cell", "DNA", "protein", "evolution", "genetics", "ecosystem", "organism", "metabolism", "photosynthesis", "microbiology", "célula", "ADN", "proteína", "evolución", "genética", "ecosistema", "organismo", "metabolismo", "fotosíntesis", "microbiología"],
     "mathematics": ["Math" "mathematics", "equation", "integral", "derivative", "function", "geometry", "algebra", "statistics", "probability", "ecuación", "integral", "derivada", "función", "geometría", "álgebra", "estadística", "probabilidad"],
     "programming": ["python", "java", "C++", "HTML", "scrip", "code", "Dataset", "API", "framework", "debugging", "algorithm", "compiler", "database", "CSS", "JSON", "XML", "encryption", "IDE", "repository", "Git", "version control", "front-end", "back-end", "API", "stack trace", "REST", "machine learning"]
 }
 
----
----
-
 class MOELLM:
     def __init__(self):
         self.current_expert = None
@@ -180,7 +180,3 @@ class MOELLM:
 if __name__ == "__main__":
     moe_llm = MOELLM()
     moe_llm.chat_interface()
-
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-https://github.com/Agnuxo1/NEBULA
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