VelocityLM

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
• 💻 Source Code: GitHub Repository

🏗️ 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 - 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

# 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 - 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

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