Instructions to use DedeProGames/DynamicMind-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/DynamicMind-MoE with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/DynamicMind-MoE", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("DedeProGames/DynamicMind-MoE", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DedeProGames/DynamicMind-MoE with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DedeProGames/DynamicMind-MoE

SGLang

How to use DedeProGames/DynamicMind-MoE 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 "DedeProGames/DynamicMind-MoE" \
    --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": "DedeProGames/DynamicMind-MoE",
		"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 "DedeProGames/DynamicMind-MoE" \
        --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": "DedeProGames/DynamicMind-MoE",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DedeProGames/DynamicMind-MoE with Docker Model Runner:
```
docker model run hf.co/DedeProGames/DynamicMind-MoE
```

DynamicMind-MoE / config.json

DedeProGames

DynamicMind-MoE: 30.2M total / 8.9M active sparse MoE, upcycled from DynamicMind-Mini

70038b6 verified about 7 hours ago

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	{
	"transformers_version": "5.5.3",
	"architectures": [
	"DynamicMindMoEForCausalLM"
	],
	"output_hidden_states": false,
	"return_dict": true,
	"dtype": "float32",
	"chunk_size_feed_forward": 0,
	"is_encoder_decoder": false,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1
	},
	"problem_type": null,
	"_name_or_path": "",
	"bos_token_id": 0,
	"eos_token_id": 0,
	"pad_token_id": 1,
	"tie_word_embeddings": true,
	"vocab_size": 8192,
	"hidden_size": 256,
	"intermediate_size": 768,
	"moe_intermediate_size": 256,
	"num_hidden_layers": 9,
	"num_attention_heads": 8,
	"num_key_value_heads": 2,
	"num_routed_experts": 14,
	"num_shared_experts": 1,
	"num_experts_per_token": 2,
	"first_k_dense_layers": 0,
	"norm_topk_prob": true,
	"router_aux_loss_coef": 0.01,
	"router_z_loss_coef": 0.001,
	"router_bias_update_rate": 0.001,
	"use_aux_loss_free_balancing": true,
	"max_position_embeddings": 1024,
	"rms_norm_eps": 1e-05,
	"rope_theta": 10000.0,
	"attention_dropout": 0.0,
	"model_type": "dynamicmind_moe",
	"output_attentions": false,
	"auto_map": {
	"AutoConfig": "configuration_dynamicmind_moe.DynamicMindMoEConfig",
	"AutoModelForCausalLM": "modeling_dynamicmind_moe.DynamicMindMoEForCausalLM"
	}
	}