README.md

---
title: TIDE-dllm
emoji: 🌊
colorFrom: blue
colorTo: indigo
sdk: static
pinned: true
tags:
- arxiv:2604.26951
---

<div align="center">
  <img src="https://huggingface.co/spaces/TIDE-dllm/README/resolve/main/logo.gif" alt="TIDE logo" width="320" />
</div>

<h1 align="center">Turning the TIDE</h1>

<p align="center"><em>Cross-Architecture Distillation for Diffusion Large Language Models</em></p>

<p align="center">🌊 The first cross-architecture distillation framework for diffusion LLMs — distilling 8B dense and 16B MoE teachers into a 0.6B student 🌊</p>

<p align="center">
  <a href="https://arxiv.org/abs/2604.26951"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2604.26951-b31b1b.svg?logo=arxiv" /></a>
  <a href="https://huggingface.co/papers/2604.26951"><img alt="HF Paper" src="https://img.shields.io/badge/%F0%9F%A4%97-Paper-blue" /></a>
  <a href="https://github.com/PKU-YuanGroup/TIDE"><img alt="Code" src="https://img.shields.io/badge/Code-PKU--YuanGroup%2FTIDE-181717.svg?logo=github" /></a>
  <a href="https://pku-yuangroup.github.io/TIDE-Page/"><img alt="Project Page" src="https://img.shields.io/badge/Project-Page-2ea44f" /></a>
  <a href="https://opensource.org/licenses/Apache-2.0"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" /></a>
</p>

---

This organization hosts the **distilled student checkpoints** and **pre-tokenized SFT datasets** released with TIDE. The framework consists of three modular components — **TIDAL** (dual-axis interpolation), **CompDemo** (complementary mask-split teacher inference), and **Reverse CALM** (cross-tokenizer chunk-level matching) — and is evaluated across two heterogeneous distillation pipelines.

## ✨ Highlights

- **+1.53 average gain** over the non-distilled BD3LM baseline across 8 benchmarks (34.20 vs. 32.67).
- **+16.48 on HumanEval** over the equivalent-size AR baseline (48.78 vs. 32.30) — distilled dLLMs especially excel at code generation.
- **22× peak-memory reduction** vs. the 16B MoE LLaDA2 teacher (1.4 GB vs. 31.3 GB) and **5.2× faster inference** (6.25 s vs. 32.55 s for 256 tokens on H100).

## 🤖 Released models

Six 0.6B distilled student checkpoints (3 per pipeline). Each is initialized from [`dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1`](https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1) and distilled from a larger dLLM teacher.

| Pipeline | Variant | Repo |
|---|---|---|
| A — Cross-Tokenizer (LLaDA2 teacher) | **TIDE-Cross** *(native, paper-best)* | [`distill-LLaDA2-TIDE_Cross`](https://huggingface.co/TIDE-dllm/distill-LLaDA2-TIDE_Cross) |
| A — Cross-Tokenizer (LLaDA2 teacher) | TIDE-Shared variant | [`distill-LLaDA2-TIDE_Shared`](https://huggingface.co/TIDE-dllm/distill-LLaDA2-TIDE_Shared) |
| A — Cross-Tokenizer (LLaDA2 teacher) | CALM baseline | [`distill-LLaDA2-CALM`](https://huggingface.co/TIDE-dllm/distill-LLaDA2-CALM) |
| B — Shared-Tokenizer (WeDLM teacher) | **TIDE-Shared** *(native, paper-best)* | [`distill-WeDLM-TIDE_Shared`](https://huggingface.co/TIDE-dllm/distill-WeDLM-TIDE_Shared) |
| B — Shared-Tokenizer (WeDLM teacher) | TIDE-Cross variant | [`distill-WeDLM-TIDE_Cross`](https://huggingface.co/TIDE-dllm/distill-WeDLM-TIDE_Cross) |
| B — Shared-Tokenizer (WeDLM teacher) | KL baseline | [`distill-WeDLM-KL`](https://huggingface.co/TIDE-dllm/distill-WeDLM-KL) |

## 📚 Released datasets

Pre-tokenized SFT mixtures (`tulu-3-sft-mixture` + `smoltalk` + `opc-sft-stage1` + `opc-sft-stage2`) prepared for each teacher, so distillation jobs never re-tokenize at startup.

| Pipeline | Repo |
|---|---|
| A — for the LLaDA2 teacher | [`distill_llada2_sft`](https://huggingface.co/datasets/TIDE-dllm/distill_llada2_sft) |
| B — for the WeDLM teacher | [`distill_wedlm_sft`](https://huggingface.co/datasets/TIDE-dllm/distill_wedlm_sft) |

## 🚀 Quick start

```python
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

repo = "TIDE-dllm/distill-LLaDA2-TIDE_Cross"   # paper-best Pipeline-A checkpoint
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForMaskedLM.from_pretrained(
    repo, dtype=torch.bfloat16, trust_remote_code=True,
).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
```

The same `generate()` routine published with [`dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1`](https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1) works on every TIDE checkpoint — just swap the model name.

## 📝 Citation

```bibtex
@misc{zhang2026turningtidecrossarchitecturedistillation,
      title={Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models},
      author={Gongbo Zhang and Wen Wang and Ye Tian and Li Yuan},
      year={2026},
      eprint={2604.26951},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.26951},
}
```