Instructions to use TIDE-dllm/distill-LLaDA2-CALM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIDE-dllm/distill-LLaDA2-CALM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TIDE-dllm/distill-LLaDA2-CALM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("TIDE-dllm/distill-LLaDA2-CALM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TIDE-dllm/distill-LLaDA2-CALM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIDE-dllm/distill-LLaDA2-CALM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIDE-dllm/distill-LLaDA2-CALM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIDE-dllm/distill-LLaDA2-CALM

SGLang

How to use TIDE-dllm/distill-LLaDA2-CALM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TIDE-dllm/distill-LLaDA2-CALM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIDE-dllm/distill-LLaDA2-CALM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TIDE-dllm/distill-LLaDA2-CALM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIDE-dllm/distill-LLaDA2-CALM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIDE-dllm/distill-LLaDA2-CALM with Docker Model Runner:
```
docker model run hf.co/TIDE-dllm/distill-LLaDA2-CALM
```

distill-LLaDA2-CALM / README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model: dllm-collection/Qwen3-0.6B-diffusion-bd3lm-v0.1
	tags:
	- diffusion
	- dllm
	- bd3lm
	- distillation
	- arxiv:2604.26951
	---

	<center> <div style="text-align: center;"> <img src="logo.gif" width="300" />
	</div> </center>

	# distill-LLaDA2-CALM

	This model was introduced in the paper [Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models](https://huggingface.co/papers/2604.26951).

	`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`](https://huggingface.co/dllm-hub/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](https://arxiv.org/abs/2604.26951) (cross-architecture distillation for diffusion LMs)
	- Framework: [TIDE / dLLM](https://github.com/PKU-YuanGroup/TIDE)
	- Student (initialization): [`Qwen3-0.6B-diffusion-bd3lm-v0.1`](https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1) (BD3LM, block_size=32)
	- Teacher: [`inclusionAI/LLaDA2.0-mini`](https://huggingface.co/inclusionAI/LLaDA2.0-mini)
	- Distillation mode: `--distill_mode alm`
	- Datasets: [tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture), [smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk), [opc-sft-stage1](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage1) and [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2) — same composition as the [`Qwen3-0.6B-diffusion-bd3lm-v0.1`](https://huggingface.co/dllm-hub/Qwen3-0.6B-diffusion-bd3lm-v0.1) base. Pre-tokenized for this teacher in [`TIDE-dllm/distill_llada2_sft`](https://huggingface.co/datasets/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`](https://huggingface.co/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](https://github.com/PKU-YuanGroup/TIDE):

	```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},
	}
	```