# Nyx: Core-Outline Transformer Model

Nyx is a transformer-based language model designed for efficient text generation and understanding. This model is part of the Core-Outline project, focusing on providing high-quality text generation capabilities with a focus on financial, SaaS, social media, customer, and customer feedback analytics data.

## Model Architecture

Nyx is built on a transformer decoder-only architecture with the following key components:

- **Rotary Position Embeddings (RoPE)**: For better handling of sequence positions
- **Multi-head Self-Attention**: With grouped-query attention for efficient inference
- **SwiGLU Activation**: For the feed-forward networks
- **RMSNorm**: For layer normalization
- **Sliding Window Attention**: For handling longer sequences efficiently

### Model Specifications

| Parameter | Value |
|-----------|-------|
| Hidden Size | 1024 |
| Number of Layers | 24 |
| Number of Attention Heads | 16 |
| Number of Key-Value Heads | 16 |
| Intermediate Size | 2816 |
| Max Sequence Length | 32,768 tokens |
| Vocabulary Size | 151,936 |
| Activation | SwiGLU (SiLU) |

## Usage

### Prerequisites

- Python 3.11+
- PyTorch 2.0+
- Transformers library
- FastAPI (for API server)

### Loading the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "core-outline/nyx"
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("core-outline/nyx")  # Using Qwen tokenizer
```

### Text Generation

```python
def generate_text(prompt, max_length=100, temperature=0.7):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs.input_ids,
        max_length=max_length,
        temperature=temperature,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
```


## Model Configuration

The model uses the following key configuration parameters (from `config.json`):

```json
{
  "hidden_size": 1024,
  "intermediate_size": 2816,
  "num_hidden_layers": 24,
  "num_attention_heads": 16,
  "num_key_value_heads": 16,
  "max_position_embeddings": 32768,
  "rms_norm_eps": 1e-6,
  "rope_theta": 1000000.0
}
```

## Tokenizer

The model uses the Qwen tokenizer, which is a BPE-based tokenizer with a vocabulary size of 151,936 tokens.

## Training Data

The model has been trained on a diverse dataset including:
- Financial analytics
- SaaS metrics
- Social media data
- Customer data
- Customer feedback

## License

[Specify your license here]

## Acknowledgements

- The model architecture is based on the Qwen/Llama architecture
- Uses Rotary Position Embeddings (RoPE) for position encoding
- Implements grouped-query attention for efficient inference