README.md

---
license: mit
tags:
- text-generation
- pytorch
- transformer
- rope
language:
- en
pipeline_tag: text-generation
library_name: pytorch
---

# VelocityLM 🚀

A high-performance, custom transformer language model trained from scratch using modern architectural innovations. VelocityLM combines state-of-the-art techniques including RMSNorm, SwiGLU activation, and Rotary Position Embeddings (RoPE) to deliver efficient and scalable language modeling.

## 🎯 Quick Links

- **🚀 Try the Model**: [Interactive Demo Space](https://huggingface.co/spaces/dixisouls/VelocityLM)
- **💻 Source Code**: [GitHub Repository](https://github.com/dixisouls/VelocityLM)

## 🏗️ Model Architecture

VelocityLM features a custom transformer architecture optimized for performance and efficiency:

### Model Specifications
- **Parameters**: ~2B parameters 
- **Architecture**: Decoder-only transformer with causal attention
- **Hidden Size**: 2,048
- **Layers**: 24 transformer layers
- **Attention Heads**: 32 heads per layer
- **Vocabulary**: 50,257 tokens (GPT-2 tokenizer compatible)
- **Context Length**: 2,048 tokens
- **Intermediate Size**: 8,192 (4x hidden size)

### 🔬 Key Innovations

#### RMSNorm (Root Mean Square Normalization)
- Replaces LayerNorm for improved training stability and efficiency
- Better gradient flow compared to traditional normalization

#### SwiGLU Activation Function
- Gated Linear Unit with Swish activation
- Superior performance compared to standard ReLU/GELU for language modeling
- Enhanced expressivity and gradient flow

#### Rotary Position Embeddings (RoPE)
- Relative position encoding with rotational invariance
- Better extrapolation capabilities to longer sequences
- More efficient than learned absolute position embeddings

## 🎯 Training Details

- **Dataset**: [Falcon RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - high-quality web text
- **Training Steps**: 5,000+ completed
- **Optimization**: AdamW with cosine annealing schedule
- **Hardware**: Trained on 4x NVIDIA A100 (80GB) GPUs
- **Features**: Mixed precision (FP16), gradient checkpointing, distributed training

## 🚀 Usage

### Basic Text Generation

```python
# Note: This model requires custom loading code
# See the GitHub repository for complete implementation

from transformers import AutoTokenizer
import torch

# Load tokenizer (GPT-2 compatible)
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# For complete usage examples and model loading:
# Visit: https://github.com/dixisouls/VelocityLM
```

### Interactive Demo
Try the model immediately in our [Hugging Face Space](https://huggingface.co/spaces/dixisouls/VelocityLM) - no setup required!

## 📊 Performance Features

### Generation Strategies
- Greedy decoding for deterministic output
- Top-k and top-p (nucleus) sampling
- Temperature control for creativity adjustment
- Repetition penalty to reduce repetitive text

### Memory Optimizations  
- Gradient checkpointing (40% memory reduction)
- Efficient causal attention implementation
- Streaming data processing

## 🔧 Technical Implementation

This model implements several cutting-edge techniques:

- **Distributed Training**: Multi-GPU support with PyTorch DDP
- **Mixed Precision**: FP16 training with automatic loss scaling  
- **Advanced Scheduling**: Cosine annealing with warm restarts
- **Memory Efficiency**: Gradient checkpointing and parameter grouping


## 🛠️ Installation & Setup

For detailed installation instructions, training scripts, and advanced usage:

**👉 Visit the [GitHub Repository](https://github.com/dixisouls/VelocityLM)**

The repository includes:
- Complete training pipeline
- Inference utilities
- Configuration management
- Multi-GPU training support
- Comprehensive documentation

## 📈 Roadmap

Future enhancements planned:
- Flash Attention 2.0 integration
- Extended context length support (4K+)
- Model quantization for efficient deployment
- Fine-tuning capabilities for downstream tasks
- ONNX export for production inference