Text Generation
Transformers
Safetensors
English
mugen
motion
human-motion
text-to-motion
motion-to-text
motion-captioning
motion-generation
autoencoder
gpt2
custom_code
Instructions to use zy22b/MUGEN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zy22b/MUGEN with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zy22b/MUGEN", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("zy22b/MUGEN", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zy22b/MUGEN with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zy22b/MUGEN" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zy22b/MUGEN", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zy22b/MUGEN
- SGLang
How to use zy22b/MUGEN 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 "zy22b/MUGEN" \ --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": "zy22b/MUGEN", "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 "zy22b/MUGEN" \ --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": "zy22b/MUGEN", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zy22b/MUGEN with Docker Model Runner:
docker model run hf.co/zy22b/MUGEN
File size: 1,606 Bytes
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"activation_function": "gelu_new",
"alae_activation": "gelu",
"alae_depth": 3,
"alae_dilation_growth_rate": 3,
"alae_dim_feedforward": 2048,
"alae_dropout": 0.05,
"alae_hidden_dim": 512,
"alae_max_decode_len": 256,
"alae_nhead": 8,
"alae_norm": null,
"alae_num_decoder_layers": 4,
"alae_num_encoder_layers": 4,
"alae_num_res_blocks": 2,
"architectures": [
"MugenForConditionalGeneration"
],
"attn_pdrop": 0.1,
"auto_map": {
"AutoConfig": "configuration_mugen.MugenConfig",
"AutoModelForCausalLM": "modeling_mugen.MugenForConditionalGeneration"
},
"bos_token_id": 50256,
"embd_pdrop": 0.1,
"eos_token_id": 50256,
"eval_sample_temperature": 1.0,
"fps": 20,
"initializer_range": 0.02,
"k_latent_slots": 2,
"latent_dim": 512,
"latent_low_rank": 64,
"layer_norm_epsilon": 1e-05,
"length_multiple": 4,
"m2t_max_new_tokens": 64,
"m2t_num_beams": 1,
"m2t_prompt_prefix": "Please describe the following human motion using plain text:",
"model_type": "mugen",
"mot_token": "<MOT>",
"mot_token_id": 50260,
"motion_input_dim": 263,
"n_embd": 768,
"n_head": 12,
"n_inner": null,
"n_layer": 12,
"n_positions": 1024,
"num_cross_attn_layers": 2,
"num_joints": 22,
"pad_token_id": 50256,
"resid_pdrop": 0.1,
"router_delta_scale": 4.0,
"router_eval_tau": 1.5,
"router_hidden": 512,
"router_static_scale": 4.0,
"t2m_prompt_template": "Please generate human motion based on the following textual description: {text} {mot}",
"torch_dtype": "float32",
"transformers_version": "4.51.0",
"vocab_size": 50261
}
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