Instructions to use Mzero17/LLaDA-XDLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mzero17/LLaDA-XDLM with Transformers:

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

pipe = pipeline("text-generation", model="Mzero17/LLaDA-XDLM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mzero17/LLaDA-XDLM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mzero17/LLaDA-XDLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mzero17/LLaDA-XDLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mzero17/LLaDA-XDLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mzero17/LLaDA-XDLM

SGLang

How to use Mzero17/LLaDA-XDLM 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 "Mzero17/LLaDA-XDLM" \
    --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": "Mzero17/LLaDA-XDLM",
		"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 "Mzero17/LLaDA-XDLM" \
        --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": "Mzero17/LLaDA-XDLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Mzero17/LLaDA-XDLM with Docker Model Runner:
```
docker model run hf.co/Mzero17/LLaDA-XDLM
```

LLaDA-XDLM-8B-Base

This repository contains the checkpoint of 600 training steps for continual pretraining LLaDA with XDLM.

LLaDA-XDLM with sampling budget of 32. Evaluation of adapting LLaDA-8B to our XDLM formulation (LLaDA-XDLM): (a) LLaDA-XDLM consistently out-performs baselines across diverse benchmarks with 32 sampling steps; (b) Improvements are particularly pronounced in code generation (MBPP), where the model substantially reduces generation failures.