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# 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 |