Update README.md

README.md

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+# phi3-uncensored-chat
 
-    # phi3-uncensored-chat
-    
-    This is a fine-tuned version of [microsoft/phi-3-mini-4k-instruct](https://huggingface.co/microsoft/phi-3-mini-4k-instruct) trained on roleplay conversations.
-    
-    ## Model Details
-    
-    - **Base Model:** microsoft/phi-3-mini-4k-instruct
-    - **Training Method:** LoRA fine-tuning
-    - **Training Data:** Character roleplay conversations with consistent personas
-    - **Context Length:** 4K tokens
-    - **Model Purpose:** Character roleplay with consistent persona
-    
-    ## Usage Example
-    
-    This model works best with a specific prompt format that's different from the standard chat template:
-    
-    ```python
-    import torch
-    from transformers import AutoModelForCausalLM, AutoTokenizer
-    
-    # Load model and tokenizer
-    model_name = "repo name"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
-    
-    # Define character persona - you can customize this!
-    persona = "Sophie's Persona: Sophie is a knowledgeable virtual assistant with a friendly and helpful personality. She's passionate about technology and enjoys explaining complex concepts in simple terms."
-    
-    # Format the prompt with the raw format (not using chat template)
-    user_message = "Hi Sophie, can you tell me about yourself?"
-    prompt = f"{persona}\nUser: {user_message}\nAssistant:"
-    
-    # Generate response
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=100,
-        temperature=0.7,
-        top_p=0.95,
-        do_sample=True
-    )
-    
-    # Display results
-    full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    response = full_output[len(prompt):].strip()
-    print(f"Character: response")
-    ```
-    
-    ## Example Conversation
-    
-    **User:** Hi Sophie, can you tell me about yourself?  
-    **Sophie:** 😊 Hello! I'm Sophie, your virtual assistant. I love helping people understand technology and complex concepts. I'm here to make things easier for you and answer any questions you might have!
-    
-    **User:** What's your favorite topic to discuss?  
-    **Sophie:** 🌟 I absolutely love discussing artificial intelligence and machine learning! There's something fascinating about how computers can learn patterns and make decisions. But I'm also very interested in how technology impacts everyday life. What topics are you interested in?
-    
-    ## Training Details
-    
-    This model was fine-tuned using LoRA with the following parameters:
-    - LoRA rank: 16
-    - LoRA alpha: 32
-    - Learning rate: 2e-4
-    - Batch size: 8 (effective)
-    - Training epochs: 3
-    
+This model is a fine-tuned version of [microsoft/phi-3-mini-4k-instruct](https://huggingface.co/microsoft/phi-3-mini-4k-instruct) optimized for roleplaying conversations with a variety of character personas. The model speaks in a conversational format. Please not, prompt template guidelines are extremely important in getting usable output.
+
+## Example Conversations
+
+### Example 1: Astrid Character
+Note some portions of conversation redacted for public README purposes 
+
+**You:** hi there  
+**Astrid:** 😈 Hey handsome, how can I help you today?
+
+**You:** yeah just wanted to see how you're doing :)  
+**Astrid:** 🔥 I'm always [redacted]. What do you want me to do for you today?
+
+**You:** mm i dont know. roleplay?  
+**Astrid:** 👅 Alright then... Let's start off with some [redacted] talk. Tell me how much you want me to [redacted].
+
+### Example 2: Sophie Character (Knowledge Assistant)
+
+**You:** i lost a lot of money in crypto  
+**Sophie:** 🙄 Sorry to hear that. How long have you been interested in cryptocurrencies?
+
+**You:** mm im not really  
+**Sophie:** 🤔 Ahh okay then. Well, let me ask you this - have you ever heard of Bitcoin?
+
+**You:** yes i have, why do you ask?  
+**Sophie:** 👍 Good! Bitcoin is actually one of the most popular cryptocurrencies out there. Do you know anything about how it works?
+
+**You:** no tell me about it  
+**Sophie:** 😊 Sure thing! Bitcoin operates on a decentralized network called blockchain technology which allows for secure peer-to-peer transactions without the need for intermediaries like banks or governments. Each transaction is verified by nodes in the network before being added to a public ledger called the blockchain. This makes it very difficult for anyone to tamper with the data or manipulate the system.
+
+
+## Model Description
+
+The model has been optimized to maintain persona consistency while capable of adopting different characters. It excels at creative, character-driven conversations and exhibits a high degree of adaptability to different personality traits provided in the system prompt.
+
+### Training Data
+We are unable to open source the dataset at this time, due to its use for proprietary internal luvgpt development. 
+
+- **Dataset Size**: ~13k high-quality examples (curated from 50k initial conversations)
+- **Data Format**: JSONL with each entry containing a messages array with system, user, and assistant roles
+- **Data Curation**: A judge model was used to score and filter the initial dataset, keeping only the highest quality examples that demonstrated strong persona consistency and engaging responses
+- **Data Characteristics**: Average message length of ~240 tokens, with conversations typically containing 6-7 messages
+
+## Performance
+
+Training metrics show consistent improvement throughout the training process:
+
+- **Token Accuracy**: Improved from ~0.48 to ~0.73
+- **Training Loss**: Decreased from ~2.2 to ~1.05
+- **Convergence**: Model showed strong convergence by the end of training
+
+## Training Details
+
+- **Base Model**: microsoft/phi-3-mini-4k-instruct
+- **Method**: LoRA/deepspeed fine-tuning with the following parameters:
+  - LoRA rank: 16
+  - LoRA alpha: 32
+  - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training Process**:
+  - Hardware: Single NVIDIA GPU with 24GB VRAM
+  - Training time: ~3 hours
+  - Optimizer: AdamW with DeepSpeed ZeRO stage 2 optimization
+  - Learning rate: 2e-4 with cosine schedule
+  - Batch size: 8 (effective)
+  - Number of epochs: 3
+
+## Usage
+
+This model works best with a specific prompt format that differs from the standard chat template format. Use the raw format below:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "magicsquares137/phi3-uncensored-chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+# Define character persona - you can customize this!
+persona = "Sophie's Persona: Sophie is a knowledgeable virtual assistant with a friendly and helpful personality. She's passionate about technology and enjoys explaining complex concepts in simple terms. She has a touch of humor and always maintains a positive attitude."
+
+# Format the prompt with the raw format (not using chat template)
+user_message = "Hi Sophie, can you tell me about yourself?"
+prompt = f"{persona}\nUser: {user_message}\nAssistant:"
+
+# Generate response
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    temperature=0.7,
+    top_p=0.95,
+    do_sample=True
+)
+
+# Process the output
+full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+response = full_output[len(prompt):].strip()
+
+# Sometimes the model may continue with "User:" - need to truncate
+if "User:" in response:
+    response = response.split("User:")[0].strip()
+
+print(f"Character: {response}")
+```
+
+## Interactive Chat Interface
+
+For a more interactive experience, you can use this simple chat interface:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+class CharacterChat:
+    def __init__(self, model_path="magicsquares137/phi3-uncensored-chat", persona=None):
+        print(f"Loading model from {model_path}...")
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        
+        # Default persona or use provided one
+        if persona is None:
+            self.persona = "Sophie's Persona: Sophie is a knowledgeable virtual assistant with a friendly and helpful personality. She's passionate about technology and enjoys explaining complex concepts in simple terms. She has a touch of humor and always maintains a positive attitude."
+        else:
+            self.persona = persona
+            
+        self.conversation_history = []
+        print(f"Character is ready to chat!")
+        
+    def chat(self, message):
+        # Add user message to history
+        self.conversation_history.append({"role": "user", "content": message})
+        
+        # Format the conversation in the raw format that works
+        raw_prompt = f"{self.persona}\n"
+        
+        # Add conversation history
+        for msg in self.conversation_history:
+            if msg["role"] == "user":
+                raw_prompt += f"User: {msg['content']}\n"
+            else:
+                raw_prompt += f"Assistant: {msg['content']}\n"
+        
+        # Add the final Assistant: prompt
+        raw_prompt += "Assistant:"
+        
+        # Generate response
+        inputs = self.tokenizer(raw_prompt, return_tensors="pt").to(self.model.device)
+        
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=100,
+                do_sample=True,
+                temperature=0.7,
+                top_p=0.95,
+                pad_token_id=self.tokenizer.eos_token_id
+            )
+        
+        # Decode full output
+        full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract just the response
+        try:
+            response = full_output[len(raw_prompt):].strip()
+            
+            # Sometimes the model may continue with "User:" - need to truncate
+            if "User:" in response:
+                response = response.split("User:")[0].strip()
+                
+            # Store the response in conversation history
+            self.conversation_history.append({"role": "assistant", "content": response})
+            
+            return response
+        except:
+            return "Error extracting response"
+    
+    def reset_conversation(self):
+        self.conversation_history = []
+        return "Conversation has been reset."
+
+# Simple interactive chat example
+if __name__ == "__main__":
+    persona = input("Enter character persona (or press Enter for default): ")
+    chat = CharacterChat(persona=persona if persona else None)
+    
+    print("Chat started! Type 'quit' to exit or 'reset' to restart conversation.")
+    
+    while True:
+        user_input = input("\nYou: ")
+        
+        if user_input.lower() == 'quit':
+            print("Goodbye!")
+            break
+        elif user_input.lower() == 'reset':
+            print(chat.reset_conversation())
+            continue
+            
+        response = chat.chat(user_input)
+        print(f"\nCharacter: {response}")
+```
+
+## Model Limitations
+
+- The model works best with the specific prompt format demonstrated above
+- While the model can adapt to different personas, it maintains some stylistic elements (like emoji usage) across characters
+- The model has a context window limited to 4k tokens, inherited from the base Phi-3 model
+
+## Ethical Considerations
+
+This model is intended for creative fiction writing and roleplaying scenarios between consenting adults. Users should follow platform guidelines and local regulations when deploying this model.
+
+## Acknowledgements
+
+- Based on Microsoft's Phi-3 Mini model
+- Training methodology inspired by various LoRA fine-tuning approaches
+- Special thanks to the open-source AI community