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license: mit
language:
- en
library_name: mlx
tags:
- mlx
- differential-transformer
- apple-silicon
- research
- ablation
---
# diff-mlx: Stage 1 paired checkpoints (Differential Transformer vs vanilla MHA)
Final checkpoints from a small-scale, controlled, paired-init reproduction of the **Differential Transformer** (Ye et al., ICLR 2025; [arXiv 2410.05258](https://arxiv.org/abs/2410.05258)), implemented in MLX on Apple Silicon with custom Metal kernels.
Code, full writeup, and methodology: **[github.com/guygrigsby/diff-mlx](https://github.com/guygrigsby/diff-mlx)**
## What's in here
| Path | Variant | Description |
|---|---|---|
| `diff/latest.safetensors` | Differential Attention | 162M params, 2.0B tokens, seed 0 |
| `vanilla/latest.safetensors` | Vanilla MHA baseline | 162M params, 2.0B tokens, seed 0 |
Each variant folder also has its `config.json` and training `metrics.jsonl`. The two models share a **byte-identical paired init** and identical data order, so the difference between them isolates the attention variant.
## Model
- Pre-norm LLaMA-style transformer: dim 768, 12 layers, interleaved RoPE, SwiGLU, RMSNorm, tied embeddings, vocab 100277 (cl100k_base).
- Context length 2048. bf16 mixed precision.
- Trained on a FineWeb-Edu sample, 2.0B tokens, effective batch 32, peak LR 4e-4, 1000-step warmup, on one M5 Max.
## The headline (the interesting part)
On held-out validation, **vanilla edges out diff** at this scale, even though diff wins on train loss:
| metric | diff | vanilla | δ (diff − vanilla) |
|---|---|---|---|
| final train loss (last 1000-step mean) | 3.0414 | 3.1526 | −0.111 (diff lower) |
| held-out val (75M tok) @ step 30000 | 3.3616 | 3.3265 | +0.035 (vanilla lower) |
Diff's train-loss lead is memorization: its val loss *rose* over the final leg while train loss kept falling. A position-binned eval put vanilla uniformly ahead across the whole 2048-token window, with no widening of diff's deficit at later positions, so the architecture's long-context edge didn't show up here either.
This sits **three orders of magnitude below** the paper's 3B-param / 1T-token setup, so it refutes nothing about the paper. It's an honest negative for this small-scale, short-context, single-seed regime. Full discussion in the repo writeup.
## Loading
```python
import mlx.core as mx
params = mx.load("diff/latest.safetensors") # or vanilla/latest.safetensors
```
Model construction lives in the repo (`model.py`, `Transformer(cfg, variant="diff"|"vanilla")`).
## License
MIT.
|