Instructions to use Zigeng/DMax-16B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zigeng/DMax-16B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zigeng/DMax-16B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Zigeng/DMax-16B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Zigeng/DMax-16B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zigeng/DMax-16B" # 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/DMax-16B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Zigeng/DMax-16B
- SGLang
How to use Zigeng/DMax-16B 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/DMax-16B" \ --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/DMax-16B", "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/DMax-16B" \ --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/DMax-16B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Zigeng/DMax-16B with Docker Model Runner:
docker model run hf.co/Zigeng/DMax-16B
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"_moe_implementation": "fused",
"architectures": [
"LLaDA2MoeModelLM"
],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_llada2_moe.LLaDA2MoeConfig",
"AutoModel": "modeling_llada2_moe.LLaDA2MoeModel",
"AutoModelForCausalLM": "modeling_llada2_moe.LLaDA2MoeModelLM"
},
"dtype": "bfloat16",
"embedding_dropout": 0.0,
"first_k_dense_replace": 1,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 5120,
"max_position_embeddings": 16384,
"max_window_layers": 28,
"model_type": "llada2_moe",
"moe_intermediate_size": 512,
"moe_router_enable_expert_bias": true,
"n_group": 8,
"norm_head": false,
"norm_softmax": false,
"norm_topk_prob": true,
"num_attention_heads": 16,
"num_experts": 256,
"num_experts_per_tok": 8,
"num_hidden_layers": 20,
"num_key_value_heads": 4,
"num_shared_experts": 1,
"output_dropout": 0.0,
"output_router_logits": false,
"pad_token_id": 156892,
"partial_rotary_factor": 0.5,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 600000,
"rotary_dim": 64,
"routed_scaling_factor": 2.5,
"router_dtype": "fp32",
"score_function": "sigmoid",
"sliding_window": 4096,
"tie_word_embeddings": false,
"topk_group": 4,
"transformers_version": "4.56.0",
"use_bias": false,
"use_cache": false,
"use_qkv_bias": false,
"use_rmsnorm": true,
"use_sliding_window": false,
"using_split_qkv_in_self_attention": false,
"vocab_size": 157184
}
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