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TIDE-dllm/distill-LLaDA2-CALM

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distill-LLaDA2-CALM

This model was introduced in the paper Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models.

distill-LLaDA2-CALM is a 0.6B diffusion language model distilled from LLaDA2.0-mini (16B MoE) into the `Qwen3-0.6B-diffusion-bd3lm-v0.1` student in the Cross-Tokenizer (Pipeline A) of the TIDE framework. Forward CALM (chunk-level approximate likelihood matching) baseline.

Model Overview

—Method: TIDE — Reverse CALM / TIDAL / CompDemo (cross-architecture distillation for diffusion LMs)
—Framework: TIDE / dLLM
—Student (initialization): `Qwen3-0.6B-diffusion-bd3lm-v0.1` (BD3LM, block_size=32)
—Teacher: `inclusionAI/LLaDA2.0-mini`
—Distillation mode: --distill_mode alm
—Datasets: tulu-3-sft-mixture, smoltalk, opc-sft-stage1 and opc-sft-stage2 — same composition as the `Qwen3-0.6B-diffusion-bd3lm-v0.1` base. Pre-tokenized for this teacher in `TIDE-dllm/distill_llada2_sft`.

Installation

shell

pip install torch transformers accelerate

Quick Start

[!NOTE] This checkpoint is fully compatible with the BD3LM generate(...) routine published with `dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1` — only the model name changes.

python

import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

repo = "TIDE-dllm/distill-LLaDA2-CALM"
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)

prompts = [
    [
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "Implement a DFS traversal in Python with clear inline comments."},
    ],
]
encoded = [tokenizer.apply_chat_template(m, add_generation_prompt=True, tokenize=True, enable_thinking=False) for m in prompts]
# ... use the same `generate()` function as in dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1.

Command-Line Interface

For an interactive demo (visualised iterative denoising), use the script in the TIDE / dLLM repo:

shell

python -u examples/a2d/bd3lm/chat.py \
    --model_name_or_path TIDE-dllm/distill-LLaDA2-CALM \
    --chat_template True --block_size 32 --remasking low_confidence \
    --steps 256 --max_new_tokens 256

Reproducing this checkpoint

shell

git clone https://github.com/PKU-YuanGroup/TIDE && cd TIDE
pip install -e . && git submodule update --init --recursive
pip install -e "lm-evaluation-harness[ifeval,math]" && pip install -e "tokenkit[full]"

# Download the pre-tokenized SFT mixture for this teacher
huggingface-cli download TIDE-dllm/distill_llada2_sft --repo-type dataset \
    --local-dir data/distill_llada2_sft

bash scripts/distill_llada2.sh \
    --data_path data/distill_llada2_sft \
    --distill_mode alm \
    --num_gpus 8

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},
}