Instructions to use zhongzero/EvoToken_LLaDA_Instruct_8B_Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhongzero/EvoToken_LLaDA_Instruct_8B_Lora with Transformers:

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

pipe = pipeline("text-generation", model="zhongzero/EvoToken_LLaDA_Instruct_8B_Lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zhongzero/EvoToken_LLaDA_Instruct_8B_Lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zhongzero/EvoToken_LLaDA_Instruct_8B_Lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhongzero/EvoToken_LLaDA_Instruct_8B_Lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhongzero/EvoToken_LLaDA_Instruct_8B_Lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zhongzero/EvoToken_LLaDA_Instruct_8B_Lora

SGLang

How to use zhongzero/EvoToken_LLaDA_Instruct_8B_Lora 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 "zhongzero/EvoToken_LLaDA_Instruct_8B_Lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhongzero/EvoToken_LLaDA_Instruct_8B_Lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "zhongzero/EvoToken_LLaDA_Instruct_8B_Lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhongzero/EvoToken_LLaDA_Instruct_8B_Lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zhongzero/EvoToken_LLaDA_Instruct_8B_Lora with Docker Model Runner:
```
docker model run hf.co/zhongzero/EvoToken_LLaDA_Instruct_8B_Lora
```

EvoTokenDLM LoRA adapter training from pretrained weights LLaDA-8B-Instruct

Starting from the original MDLM (Masked Discrete Diffusion Language Model) LLaDA-8B-Instruct, we trained the EvoTokenDLM LoRA adapter using the Continuous Trajectory Supervision method.

Our implementation replaces traditional hard binary masks with evolving soft token distributions. This allows EvoTokenDLM to facilitate a progressive transition from masked states to discrete outputs, effectively supporting revisable decoding.

The method and its results are detailed in the paper: Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models.

How to Use

⚠️ Important: This is a LoRA adapter and requires the official EvoTokenDLM codebase for inference.

For detailed instructions and code, please refer to the official GitHub repository: EvoTokenDLM GitHub Repository

Citation

If you find this work helpful for your research, please cite:

@article{zhong2026beyond,
    title={Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models},
    author={Zhong, Linhao and Wu, Linyu and Fang, Bozhen and Feng, Tianjian and Jing, Chenchen and Wang, Wen and Zhang, Jiaheng and Chen, Hao and Shen, Chunhua},
    journal={arXiv preprint arXiv:2601.07351},
    year={2026}
}

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Paper for zhongzero/EvoToken_LLaDA_Instruct_8B_Lora

Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models

Paper • 2601.07351 • Published Jan 12 • 26