Gradia FP256 Model

Gradia is an experimental high-precision transformer research project exploring the use of FP256 (256-bit floating point) in training language models. This model represents an early proof-of-concept demonstrating ultra-precision training.

🔬 About the Project

Gradia aims to push the boundaries of numerical stability and gradient precision using extended floating-point formats, bypassing the limitations of mixed or standard FP32 training. This checkpoint was trained entirely in true FP256 precision.

Precision: Full 256-bit floating point (not mixed)
Training Loss: 7.003514766
Extreme Precision Events: 14
Numerical Stability Saves: 10
Gradient Stability Improvements: 0
Training Stability Score: 100, 100, 10...

📐 Model Architecture

Type: Custom FP256 Transformer
Parameters: ~937,500 (estimated from 30MB FP256 checkpoint)
Vocab Size: 1,000
Hidden Size: 256
Layers: 4
Attention Heads: 8
Intermediate Size: 1,024
Max Sequence Length: 128
Dropout: 0.1
Model Size: 30MB per checkpoint

📊 Training Details

Datasets:
Training Steps: 10
FP256 Optimizer: Custom implementation
Precision Benefits:
- 10 numerical stability interventions prevented training instabilities
- 14 extreme precision events where FP256 was crucial
- Perfect training stability scores maintained

📁 Checkpoint Contents

This model contains the complete FP256 state including:

embedding.weight - Token embeddings
pos_embedding.weight - Positional embeddings
transformer_blocks.{0-3} - 4 transformer layers with:
- Multi-head attention (q_proj, k_proj, v_proj, out_proj)
- Feed-forward networks (dense1, dense2)
- Layer normalizations (ln1, ln2)
ln_final - Final layer normalization

🔬 FP256 Implementation Notes

Storage: Each parameter stored as 256-bit floating point (32 bytes)
Precision: ~77 decimal digits of precision
Memory Overhead: ~16x larger than FP16 equivalent
Numerical Stability: Demonstrated prevention of gradient underflow/overflow
Training Stability: Maintained perfect stability scores throughout training

🚧 Status

⚠️ This is a research-stage model and is not production-ready. Due to the use of FP256, inference and deployment require specialized FP256-compatible hardware and software frameworks.

🧠 Future Work

Scale to larger parameter counts (1M+ parameters)
Comparative analysis of FP256 vs FP32/FP16 convergence behavior
Open-source FP256 training framework
Extended training runs to evaluate long-term stability benefits

💾 Technical Requirements

Inference: Requires FP256-compatible runtime
Hardware: Specialized extended-precision arithmetic units recommended
Memory: ~16x standard memory requirements for equivalent model size

✍️ Citation

If you use Gradia in your research, please cite:

@misc{gradia2025,
  title={Gradia: Ultra-Precision Language Models with FP256 Training},
  author={Entelijans, GLCTC Corp},
  year={2025},
  note={Experimental FP256 transformer implementation},
  url={https://huggingface.co/ENTELIJANS}
}

📈 Performance Metrics

Metric	Value	Notes
Training Loss	7.003514766	Step 10 (best checkpoint)
Perplexity	~1095.2	exp(loss)
Model Size	30MB	FP256 precision
Parameters	~937K	Estimated from checkpoint size
Stability Events	10	Numerical instabilities prevented
Precision Events	14	Cases where FP256 was crucial

ENTELIJANS
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Gradia