Instructions to use StarpowerTechnology/WVY-Liquid-Recurrent-Depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StarpowerTechnology/WVY-Liquid-Recurrent-Depth with Transformers:

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

pipe = pipeline("text-generation", model="StarpowerTechnology/WVY-Liquid-Recurrent-Depth")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("StarpowerTechnology/WVY-Liquid-Recurrent-Depth", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use StarpowerTechnology/WVY-Liquid-Recurrent-Depth with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/StarpowerTechnology/WVY-Liquid-Recurrent-Depth

SGLang

How to use StarpowerTechnology/WVY-Liquid-Recurrent-Depth 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 "StarpowerTechnology/WVY-Liquid-Recurrent-Depth" \
    --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": "StarpowerTechnology/WVY-Liquid-Recurrent-Depth",
		"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 "StarpowerTechnology/WVY-Liquid-Recurrent-Depth" \
        --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": "StarpowerTechnology/WVY-Liquid-Recurrent-Depth",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use StarpowerTechnology/WVY-Liquid-Recurrent-Depth with Docker Model Runner:
```
docker model run hf.co/StarpowerTechnology/WVY-Liquid-Recurrent-Depth
```

WVY-Liquid-Recurrent-Depth / reference /transformers-lfm2 /__init__.py

StarpowerTechnology

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	# Copyright 2025 The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	from typing import TYPE_CHECKING

	from ...utils import _LazyModule
	from ...utils.import_utils import define_import_structure


	if TYPE_CHECKING:
	from .configuration_lfm2 import *
	from .modeling_lfm2 import *
	else:
	import sys

	_file = globals()["__file__"]
	sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)