Instructions to use RHYu2233/LaCT-Motion-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RHYu2233/LaCT-Motion-SFT with Transformers:

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

pipe = pipeline("text-generation", model="RHYu2233/LaCT-Motion-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RHYu2233/LaCT-Motion-SFT")
model = AutoModelForCausalLM.from_pretrained("RHYu2233/LaCT-Motion-SFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RHYu2233/LaCT-Motion-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RHYu2233/LaCT-Motion-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RHYu2233/LaCT-Motion-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RHYu2233/LaCT-Motion-SFT

SGLang

How to use RHYu2233/LaCT-Motion-SFT 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 "RHYu2233/LaCT-Motion-SFT" \
    --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": "RHYu2233/LaCT-Motion-SFT",
		"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 "RHYu2233/LaCT-Motion-SFT" \
        --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": "RHYu2233/LaCT-Motion-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RHYu2233/LaCT-Motion-SFT with Docker Model Runner:
```
docker model run hf.co/RHYu2233/LaCT-Motion-SFT
```

LaCT-Motion SFT checkpoint (`checkpoint-epoch6`)

Curriculum supervised fine-tuning (SFT) checkpoint of LaCT-Motion (Latent Chain-of-Thought for Motion), the official implementation of the ECCV 2026 paper Practice Makes Perfect: From Explicit Decomposition to Reinforced Latent Planning in Text-to-Motion Generation.

Code: https://github.com/Erwin2233/LaCT-Motion
Paper (PDF): https://media.eventhosts.cc/Conferences/ECCV2026/pdfs/13122.pdf

This is the checkpoint referenced by the GRPO configurations in the code repository: it initializes the Stage 2 GRPO run that refines the fully latent policy.

Built with Qwen. This model is a fine-tuned derivative of Qwen2.5-3B-Instruct and is released under the Qwen RESEARCH LICENSE AGREEMENT (non-commercial research and evaluation use only). See NOTICE.

Model details

Item	Value
Base model	Qwen/Qwen2.5-3B-Instruct (Qwen2ForCausalLM, 36 layers, hidden size 2048)
Vocabulary	152,184 tokens: the Qwen vocabulary extended with 512 motion codes (`<Motion_0>` … `<Motion_511>`), motion delimiters, and latent-reasoning special tokens
Latent settings	`c_thought: 2`, `max_latent_stage: 8`, `pad_latent_to_max: true` (16 latent tokens per prompt)
Precision	bfloat16 safetensors, two shards
Motion tokenizer	Motion VQ-VAE with 512 codes (`ckpt/vqvae.pth` in the code repository, from the Motion-Agent release)
Training data	HumanML3D, processed splits from `data/` in the code repository

The checkpoint contains the model weights, configuration, and tokenizer only; the SFT optimizer state is not included.

Usage

Download it into the location expected by the code repository:

hf download RHYu2233/LaCT-Motion-SFT --local-dir checkpoints/sft/checkpoint-epoch6

Then follow the repository README to run GRPO from this checkpoint, evaluate it on HumanML3D, or generate motions with the demo:

python eval_t2m.py options/sft/t2m_coconut.yaml \
  --checkpoint checkpoints/sft/checkpoint-epoch6 \
  --batch-size 32 --repeat 1 --device cuda:0 \
  --output results/t2m_metrics.json

Generation requires the motion VQ-VAE, the T2M evaluator, and the GloVe files described in the repository README; the model outputs motion codes that the VQ-VAE decodes into HumanML3D joint features.

License

The weights are distributed under the Qwen RESEARCH LICENSE AGREEMENT inherited from the base model Qwen2.5-3B-Instruct: non-commercial research and evaluation use only; commercial use requires a license from Alibaba Cloud. The agreement is included as LICENSE, and the required attribution notice is in NOTICE. The training data derives from HumanML3D (AMASS and HumanAct12), which are likewise restricted to non-commercial research use.

Citation

@inproceedings{yu2026lactmotion,
  title     = {Practice Makes Perfect: From Explicit Decomposition to Reinforced Latent Planning in Text-to-Motion Generation},
  author    = {Yu, Ronghao and Liu, Yang and Wang, Juncheng and Xu, Chao and Shao, Yimo and Sun, Baigui and Liu, Yong and Luo, Shan},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for RHYu2233/LaCT-Motion-SFT

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct