README.md

---
language: en
tags:
  - mask-predict
  - diffusion
  - masked-lm
library_name: transformers
base_model: philipp-zettl/modernbert-diffusion-universal
pipeline_tag: fill-mask
---

# ./refinebert-refactor

## Model Summary
A diffusion-style masked language model fine-tuned from `philipp-zettl/modernbert-diffusion-universal` on the `custom` dataset.

## Model Details
- **Model ID:** ./refinebert-refactor
- **Base model:** philipp-zettl/modernbert-diffusion-universal
- **Training mode:** Fine-tuning
- **Task type:** Masked token denoising / diffusion-style infilling

## Intended Use
Intended for tasks related to the custom training data.

**Example**
```python
from refinebert.diffusion_engine import MaskedDiffusionEngine

engine = MaskedDiffusionEngine("./refinebert-refactor")
prompt = "N/A (See generation logs)"
output = engine.generate(prompt, num_new_tokens=N/A, steps=N/A, guidance_scale=N/A)
print(output)
```

## Training Data
Single-dataset fine-tuning.

### Dataset Mix
| Custom Files | 100% | code_refactoring.txt |

Fine-tuned on user-provided local text files.

## Training Procedure
- **Steps:** 1731
- **Batch size:** 16
- **Sequence length:** 256
- **Learning rate:** 5e-05
- **CFG dropout probability:** N/A
- **Samples loaded into RAM:** N/A

## Training Time & Hardware
- **Duration:** 0h 10m 25s
- **Hardware:** NVIDIA GeForce RTX 4070 Laptop GPU x1 (CUDA available)

## Metrics (Training)
| Metric | Value |
| --- | --- |
| Training Loss | 2.0958 |
| Epochs | 3 |
| Global Step | 1731 |

## Limitations & Considerations
- The model is trained with a masked-token diffusion objective and may not behave like an autoregressive LM.
- Data sources may have licensing or content constraints—review source dataset cards before deployment.
- Performance can vary substantially by mode (Fine-tuning) and prompt structure.