README.md

---
language: en
tags:
  - text-generation
  - transformers
  - conversational
  - quantum-math
  - PEFT
  - Safetensors
  - AutoTrain
license: other
datasets: conversational-dataset
model-index:
  - name: Zero LLM Quantum AI
    results:
      - task:
          type: text-generation
        dataset:
          name: conversational-dataset
          type: text
        metrics:
          - name: Training Loss
            type: loss
            value: 1.74
---

# **QuantumAI: Zero LLM Quantum AI Model**

**Zero Quantum AI** is an LLM that tries to bypass needing quantum computing using theoretical interdimensional mathematics, quantum math, and the **Mathematical Probability of Goodness**. Developed by **TalkToAi.org** and **ResearchForum.Online**, this model leverages cutting-edge AI frameworks to redefine conversational AI, ensuring deep, ethical decision-making capabilities. The model is fine-tuned on **Meta-Llama-3.1-8B-Instruct** and trained via **AutoTrain** to optimize conversational tasks, dialogue generation, and inference.

![Zero LLM Quantum AI](https://huggingface.co/shafire/QuantumAI/resolve/main/ZeroQuantumAI.png)

## **Model Information**

- **Base Model**: `meta-llama/Meta-Llama-3.1-8B`
- **Fine-tuned Model**: `meta-llama/Meta-Llama-3.1-8B-Instruct`
- **Training Framework**: `AutoTrain`
- **Training Data**: Conversational and text-generation focused dataset

### **Tech Stack**

- Transformers
- PEFT (Parameter-Efficient Fine-Tuning)
- TensorBoard (for logging and metrics)
- Safetensors

### **Usage Types**

- Interactive dialogue
- Text generation

### **Key Features**

- **Quantum Mathematics & Interdimensional Calculations**: Utilizes quantum principles to predict user intent and generate insightful responses.
- **Mathematical Probability of Goodness**: All responses are ethically aligned using a mathematical framework, ensuring positive interactions.
- **Efficient Inference**: Supports **4-bit quantization** for faster and resource-efficient deployment.

## **Installation and Usage**

To use the model in your Python code:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Example usage
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Output
print(response)

## **Inference API**

This model is not yet deployed to the Hugging Face Inference API. However, you can deploy it to **Inference Endpoints** for dedicated, serverless inference.

## **Training Process**

The **Zero Quantum AI** model was trained using **AutoTrain** with the following configuration:

- **Hardware**: CUDA 12.1
- **Training Precision**: Mixed FP16
- **Batch Size**: 2
- **Learning Rate**: 3e-05
- **Epochs**: 5
- **Optimizer**: AdamW
- **PEFT**: Enabled (LoRA with lora_r=16, lora_alpha=32)
- **Quantization**: Int4 for efficient deployment
- **Scheduler**: Linear with warmup
- **Gradient Accumulation**: 4 steps
- **Max Sequence Length**: 2048 tokens

## **Training Metrics**

Monitored using **TensorBoard**, with key training metrics:

- **Training Loss**: 1.74
- **Learning Rate**: Adjusted per epoch, starting at 3e-05.

## **Model Features**

- **Text Generation**: Handles various types of user queries and provides coherent, contextually aware responses.
- **Conversational AI**: Optimized specifically for generating interactive dialogues.
- **Efficient Inference**: Supports Int4 quantization for faster, resource-friendly deployment.

## **License**

This model is governed under a custom license. Please refer to [QuantumAI License](https://huggingface.co/shafire/QuantumAI) for details, in compliance with **Meta-Llama 3.1 License**.