Update README.md

README.md

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 This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+## How the MOE System Works
+
+This model is a core component of a larger Multi-Expert Question Answering System. Here's a breakdown of the system's functionality:
+
+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.
+
+This MOE approach enhances efficiency and accuracy compared to relying on a single, general-purpose LLM.
+
+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
+
+
+## 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
+
+# Global parameters for each model
+MODEL_PARAMS = {
+    "director": {
+        "temperature": 0.7,  # Adjust as needed
+        "max_tokens": 25     # Adjust as needed
+    },
+    "programming": {
+        "temperature": 0.5,
+        "max_tokens": 200
+    },
+    "biology": {
+        "temperature": 0.5,
+        "max_tokens": 200
+    },
+    "mathematics": {
+        "temperature": 0.5,
+        "max_tokens": 200
+    }
+}
+
+# Model configuration
+MODEL_CONFIG = {
+    "director": {
+        "name": "Agnuxo/Qwen2_0.5B_Spanish_English_raspberry_pi_16bit",
+        "task": "text-generation",
+    },
+    "programming": {
+        "name": "Qwen/Qwen2-1.5B-Instruct",
+        "task": "text-generation",
+    },
+    "biology": {
+        "name": "Agnuxo/Qwen2-1.5B-Instruct_MOE_BIOLOGY_assistant_16bit",
+        "task": "text-generation",
+    },
+    "mathematics": {
+        "name": "Qwen/Qwen2-Math-1.5B-Instruct",
+        "task": "text-generation",
+    }
+}
+
+# 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
+        self.current_model = None
+        self.current_tokenizer = None
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"Using device: {self.device}")
+        self.load_director_model()
+
+    def load_director_model(self):
+        """Loads the director model."""
+        print("Loading director model...")
+        model_name = MODEL_CONFIG["director"]["name"]
+        self.director_tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.director_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).to(self.device)
+        print("Director model loaded.")
+
+    def load_expert_model(self, expert):
+        """Dynamically loads an expert model, releasing memory from the previous model."""
+        if expert not in MODEL_CONFIG:
+            raise ValueError(f"Unknown expert: {expert}")
+
+        if self.current_expert != expert:
+            print(f"Loading expert model: {expert}...")
+            
+            # Free memory from the current model if it exists
+            if self.current_model:
+                del self.current_model
+                del self.current_tokenizer
+                torch.cuda.empty_cache()
+            
+            model_config = MODEL_CONFIG[expert]
+            self.current_tokenizer = AutoTokenizer.from_pretrained(model_config["name"])
+            self.current_model = AutoModelForCausalLM.from_pretrained(model_config["name"], torch_dtype=torch.float16).to(self.device)
+            self.current_expert = expert
+            
+            print(f"{expert.capitalize()} model loaded.")
+
+    def determine_expert_by_keywords(self, question):
+        """Determines the expert based on keywords in the question."""
+        question_lower = question.lower()
+        for expert, keywords in KEYWORDS.items():
+            if any(keyword in question_lower for keyword in keywords):
+                return expert
+        return None
+
+    def determine_expert(self, question):
+        """Determines which expert should answer the question."""
+        expert = self.determine_expert_by_keywords(question)
+        if expert:
+            print(f"Expert determined by keyword: {expert}")
+            return expert
+
+        prompt = f"Classify the following question into one of these categories: programming, biology, mathematics. Question: {question}\nCategory:"
+        response = self.director_model.generate(
+            **self.director_tokenizer(prompt, return_tensors="pt").to(self.device),
+            max_new_tokens=MODEL_PARAMS["director"]["max_tokens"],
+            temperature=MODEL_PARAMS["director"]["temperature"],
+            num_return_sequences=1
+        )
+        response_text = self.director_tokenizer.decode(response[0], skip_special_tokens=True)
+        expert = response_text.split(":")[-1].strip().lower()
+        if expert not in MODEL_CONFIG:
+            expert = "director"
+        print(f"Redirecting question to: {expert}")
+        return expert
+
+    def generate_response(self, question, expert):
+        """Generates a response using the appropriate model."""
+        try:
+            self.load_expert_model(expert)
+            prompt = f"Answer the following question as an expert in {expert}: {question}\nAnswer:"
+            
+            if expert == "director":
+                model = self.director_model
+                tokenizer = self.director_tokenizer
+            else:
+                model = self.current_model
+                tokenizer = self.current_tokenizer
+            
+            response = model.generate(
+                **tokenizer(prompt, return_tensors="pt").to(self.device),
+                max_new_tokens=MODEL_PARAMS[expert]["max_tokens"],
+                temperature=MODEL_PARAMS[expert]["temperature"],
+                num_return_sequences=1
+            )
+            response_text = tokenizer.decode(response[0], skip_special_tokens=True)
+            return response_text.split("Answer:")[-1].strip()
+        except Exception as e:
+            print(f"Error generating response: {str(e)}")
+            return "Sorry, there was an error processing your request. Please try again."
+
+    def chat_interface(self):
+        """Simple chat interface."""
+        print("Welcome to the MOE-LLM chat. Type 'exit' to quit.")
+        while True:
+            question = input("\nYou: ")
+            if question.lower() in ['exit', 'quit']:
+                break
+            
+            try:
+                expert = self.determine_expert(question)
+                response = self.generate_response(question, expert)
+                print(f"\n{expert.capitalize()}: {response}")
+            except Exception as e:
+                print(f"Error in chat: {str(e)}")
+                print("Please try asking another question.")
+
+if __name__ == "__main__":
+    moe_llm = MOELLM()
+    moe_llm.chat_interface()  
+