---
base_model:
- MBZUAI/LaMini-GPT-774M
library_name: transformers
license: apache-2.0
model_name: ChatGPT-2.V2
tags:
- conversational-ai
- fine-tuning
- gpt2
- causal-lm
- chatbots
---

# ChatGPT-2.V2 Model Card

## Model Description

**ChatGPT-2.V2** is a fine-tuned version of the **lamini-gpt-774M** instruction model, optimized for conversational AI tasks. The model is trained to generate coherent, context-aware responses for interactive chatbot applications, achieving significant improvements in performance through fine-tuning on a combination of public conversational datasets and curated, domain-specific datasets. 

This model supports a context length of up to **1024 tokens**, enabling it to handle multi-turn conversations effectively.

---

## Fine-Tuning Process

The model was fine-tuned using **public conversational datasets** and **curated datasets** specifically tailored for interactive chat scenarios. The fine-tuning process aimed to:

- Enhance the model's ability to understand and respond to diverse conversational prompts.
- Improve context retention and relevance in multi-turn interactions.
- Achieve a balance between creativity and accuracy for engaging chatbot responses.

The training process resulted in a **final loss of 1.2**, indicating strong convergence and performance.

---

## Key Features

- **Conversational Proficiency:** Designed for real-time chat applications with context-aware responses.
- **Fine-Tuned Context Handling:** Supports up to 1024 tokens, enabling robust multi-turn conversations.
- **Instruction-Based Foundation:** Built on the lamini-gpt-774M instruction model, retaining its strengths in task-oriented dialogues.

---

## Training Details

- **Base Model:** lamini-gpt-774M
- **Fine-Tuning Framework:** Hugging Face Transformers
- **Datasets Used:**
  - Public conversational datasets (open-domain)
  - Custom curated datasets for domain-specific conversations
- **Context Length:** 1024 tokens
- **Final Loss:** 1.2
- **Learning Rate:** 1e-5
- **Training Epochs:** 3
- **fp16:** True

---

## Usage

The model is intended for conversational AI applications, such as:

- Chatbots for customer support
- Interactive virtual assistants
- Personalized conversational agents
  
### Inference Example

```python
# Load model directly
from transformers import AutoModelForCausalLM, GPT2Tokenizer
import torch

tokenizer = GPT2Tokenizer.from_pretrained("suriya7/ChatGPT-2.V2")
model = AutoModelForCausalLM.from_pretrained("suriya7/ChatGPT-2.V2")

prompt = """
<|im_start|>system\nYou are a helpful AI assistant named Securitron, trained by Aquilax.<|im_end|>
"""

# Keep a list for the last one conversation exchanges
conversation_history = []

while True:
    user_prompt = input("User Question: ")
    if user_prompt.lower() == 'break':
        break

    # Format the user's input
    user = f"""<|im_start|>user
{user_prompt}<|im_end|>"""

    # Add the user's question to the conversation history
    conversation_history.append(user)

    # Ensure conversation starts with a user's input and keep only the last 2 exchanges (4 turns)
    conversation_history = conversation_history[-5:]

    # Build the full prompt
    current_prompt = prompt + "\n".join(conversation_history)

    # Tokenize the prompt
    encodeds = tokenizer(current_prompt, return_tensors="pt", truncation=True).input_ids

    # Move model and inputs to the appropriate device
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    model.to(device)
    inputs = encodeds.to(device)

    # Create an empty list to store generated tokens
    generated_ids = inputs

    # Start generating tokens one by one
    assistant_response = ""
    for _ in range(512):  # Specify a max token limit for streaming
        next_token = model.generate(
            generated_ids,
            max_new_tokens=1,
            pad_token_id=50259,
            eos_token_id=50259,
            num_return_sequences=1,
            do_sample=True,
            top_k=50,
            temperature=0.2,
            top_p=0.90
        )
        
        generated_ids = torch.cat([generated_ids, next_token[:, -1:]], dim=1)
        token_id = next_token[0, -1].item()
        token = tokenizer.decode([token_id], skip_special_tokens=True)
        
        assistant_response += token
        print(token, end="", flush=True)

        if token_id == 50259:  # EOS token
            break

    print()
    conversation_history.append(f"<|im_start|>{assistant_response.strip()}<|im_end|>")
```

## Limitations
While the model performs well in general chat scenarios, it may encounter challenges in:

- Highly domain-specific contexts not covered during fine-tuning.
- Very long conversations that exceed the 1024-token context limit.

## Additional Disclaimer

Please note that this model has not been specifically aligned using techniques such as Direct Preference Optimization (DPO) or similar methodologies. While the model has been fine-tuned to perform well in chat-based tasks, its responses are not guaranteed to reflect human-aligned preferences or ethical guidelines. Use with caution in sensitive or critical applications.