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")

# 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 / scripts /prepare_data_and_model.sh

StarpowerTechnology

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262eb2c verified 25 days ago

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	#!/usr/bin/env bash
	set -euo pipefail

	if [ "$#" -lt 1 ]; then
	echo "Usage: scripts/prepare_data_and_model.sh DATA_PATH [VOCAB_SIZE] [TOKENS_PER_PARAMETER]"
	exit 2
	fi

	data_path="$1"
	vocab_size="${2:-8192}"
	ratio="${3:-20}"

	wvy-tokenizer --data "$data_path" --vocab-size "$vocab_size" --output artifacts/tokenizer
	wvy-scan --data "$data_path" --tokenizer artifacts/tokenizer --tokens-per-parameter "$ratio" --output artifacts/data_report.json
	wvy-build --report artifacts/data_report.json --tokenizer artifacts/tokenizer --output artifacts/model_config.json

	echo "Prepared artifacts/tokenizer, artifacts/data_report.json, and artifacts/model_config.json"