Instructions to use Zigeng/dParallel-LLaDA-8B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zigeng/dParallel-LLaDA-8B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Zigeng/dParallel-LLaDA-8B-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Zigeng/dParallel-LLaDA-8B-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zigeng/dParallel-LLaDA-8B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zigeng/dParallel-LLaDA-8B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zigeng/dParallel-LLaDA-8B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zigeng/dParallel-LLaDA-8B-instruct

SGLang

How to use Zigeng/dParallel-LLaDA-8B-instruct 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 "Zigeng/dParallel-LLaDA-8B-instruct" \
    --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": "Zigeng/dParallel-LLaDA-8B-instruct",
		"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 "Zigeng/dParallel-LLaDA-8B-instruct" \
        --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": "Zigeng/dParallel-LLaDA-8B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zigeng/dParallel-LLaDA-8B-instruct with Docker Model Runner:
```
docker model run hf.co/Zigeng/dParallel-LLaDA-8B-instruct
```

dParallel-LLaDA-8B-instruct

File size: 1,544 Bytes

5e138f6

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  "_name_or_path": "GSAI-ML/LLaDA-8B-Instruct",
  "activation_type": "silu",
  "alibi": false,
  "alibi_bias_max": 8.0,
  "architectures": [
    "LLaDAModelLM"
  ],
  "attention_dropout": 0.0,
  "attention_layer_norm": false,
  "attention_layer_norm_with_affine": true,
  "auto_map": {
    "AutoConfig": "GSAI-ML/LLaDA-8B-Instruct--configuration_llada.LLaDAConfig",
    "AutoModel": "GSAI-ML/LLaDA-8B-Instruct--modeling_llada.LLaDAModelLM",
    "AutoModelForCausalLM": "GSAI-ML/LLaDA-8B-Instruct--modeling_llada.LLaDAModelLM"
  },
  "bias_for_layer_norm": false,
  "block_group_size": 1,
  "block_type": "llama",
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  "embedding_size": 126464,
  "eos_token_id": 126081,
  "flash_attention": false,
  "include_bias": false,
  "include_qkv_bias": false,
  "init_cutoff_factor": null,
  "init_device": "meta",
  "init_fn": "mitchell",
  "init_std": 0.02,
  "input_emb_norm": false,
  "layer_norm_type": "rms",
  "layer_norm_with_affine": true,
  "mask_token_id": 126336,
  "max_sequence_length": 4096,
  "mlp_hidden_size": 12288,
  "mlp_ratio": 4,
  "model_type": "llada",
  "multi_query_attention": null,
  "n_heads": 32,
  "n_kv_heads": 32,
  "n_layers": 32,
  "pad_token_id": 126081,
  "precision": "amp_bf16",
  "residual_dropout": 0.0,
  "rms_norm_eps": 1e-05,
  "rope": true,
  "rope_full_precision": true,
  "rope_theta": 500000.0,
  "scale_logits": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.49.0",
  "use_cache": false,
  "vocab_size": 126464,
  "weight_tying": false
}