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---
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 |