ENTELIJANS
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Gradia

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-{}
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 ---
+language: en
+license: apache-2.0
+tags:
+  - fp256
+  - ultra-precision
+  - transformer
+  - experimental
+  - research
+datasets:
+  - interstellarninja/hermes_reasoning_tool_use
+  - NousResearch/Hermes-3-Dataset
+library_name: transformers
+model-index:
+  - name: Gradia FP256 Series
+    results: []
 ---
+# Gradia FP256 Model — Checkpoint 20
+Gradia is an experimental high-precision transformer research project exploring the use of **FP256 (256-bit floating point)** in training language models. This model is part of an early proof-of-concept run.
+## 🔬 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 (Step 20) was trained entirely in **true FP256 precision**, with a model size of ~500K parameters.
+- **Precision**: Full 256-bit (not mixed)
+- **Loss (Final)**: `6.97254610`
+- **Extreme Precision Events Logged**: `28`
+- **Numerical Stability Events**: `20`
+- **Gradient Stability Improvements**: `0` (indicating raw gradient tracking)
+## 📐 Model Architecture
+- **Type**: Transformer (custom)
+- **Parameters**: 501,628
+- **Layers**: 2 (assumed, based on parameter count and logs)
+- **Embedding**: Positional + Token
+- **Checkpoint Format**: PyTorch `.pt`
+## 📊 Training Details
+- **Datasets**:
+  - [interstellarninja/hermes_reasoning_tool_use](https://huggingface.co/datasets/interstellarninja/hermes_reasoning_tool_use)
+  - [NousResearch/Hermes-3-Dataset](https://huggingface.co/datasets/NousResearch/Hermes-3-Dataset)
+- **Steps**: 20
+- **Batch Size**: [specify if known]
+- **Optimizer**: [specify if Adam, SGD, etc.]
+- **Scheduler**: [specify type if known]
+- **Loss Function**: [specify, e.g. CrossEntropyLoss]
+## 📁 Checkpoints
+This repo contains:
+- `checkpoint_10.pt`
+- `checkpoint_20.pt`
+- `best_model.pt` (selected based on lowest loss)
+## 🚧 Status
+> ⚠️ This is a **research-stage model** and is **not production-ready**. Due to the use of FP256, inference and deployment require special tooling and hardware support.
+## 🧠 Future Work
+- Larger parameter models (10M–1B) in FP256
+- Analysis of convergence behavior vs FP32/FP16
+- Open-source FP256 simulator tooling
+## ✍️ Citation
+If you use Gradia in your research, please cite:
+```bibtex
+@misc{gradia2025,
+  title={Gradia: Ultra-Precision Language Models in FP256},
+  author={The Gradia Project Contributors},
+  year={2025},
+  note={https://huggingface.co/Gradia}
+}