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

Libraries

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

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

pipe = pipeline("text-generation", model="TIDE-dllm/distill-LLaDA2-TIDE_Cross", 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-TIDE_Cross", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TIDE-dllm/distill-LLaDA2-TIDE_Cross 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-TIDE_Cross"
# 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-TIDE_Cross",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use TIDE-dllm/distill-LLaDA2-TIDE_Cross 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-TIDE_Cross" \
    --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-TIDE_Cross",
		"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-TIDE_Cross" \
        --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-TIDE_Cross",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

N2048M commited on Apr 30

Commit

41d79bf

verified ·

1 Parent(s): 8b39cd2

Add arxiv:2604.26951 tag to link the paper for HF indexing

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README.md +1 -0

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@@ -8,6 +8,7 @@ tags:
 - dllm
 - bd3lm
 - distillation
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 <center> <div style="text-align: center;"> <img src="logo.gif" width="300" />

 - dllm
 - bd3lm
 - distillation
+- arxiv:2604.26951
 ---
 <center> <div style="text-align: center;"> <img src="logo.gif" width="300" />