Instructions to use ayh015/myLightningOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayh015/myLightningOPD with Transformers:

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

pipe = pipeline("text-generation", model="ayh015/myLightningOPD")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayh015/myLightningOPD")
model = AutoModelForCausalLM.from_pretrained("ayh015/myLightningOPD", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ayh015/myLightningOPD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayh015/myLightningOPD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayh015/myLightningOPD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayh015/myLightningOPD

SGLang

How to use ayh015/myLightningOPD 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 "ayh015/myLightningOPD" \
    --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": "ayh015/myLightningOPD",
		"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 "ayh015/myLightningOPD" \
        --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": "ayh015/myLightningOPD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ayh015/myLightningOPD with Docker Model Runner:
```
docker model run hf.co/ayh015/myLightningOPD
```

myLightningOPD / setup.py

ayh015

Upload setup.py with huggingface_hub

ff582c5 verified about 1 month ago

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	# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
	# SPDX-License-Identifier: Apache-2.0

	import sys
	import platform

	from setuptools import find_packages, setup
	from wheel.bdist_wheel import bdist_wheel as _bdist_wheel


	def _fetch_requirements(path):
	with open(path, "r") as fd:
	return [r.strip() for r in fd.readlines() if r.strip() and not r.startswith("#")]


	# Custom wheel class to modify the wheel name
	class bdist_wheel(_bdist_wheel):
	def finalize_options(self):
	_bdist_wheel.finalize_options(self)
	self.root_is_pure = False

	def get_tag(self):
	python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
	abi_tag = f"{python_version}"

	if platform.system() == "Linux":
	platform_tag = "manylinux1_x86_64"
	else:
	platform_tag = platform.system().lower()

	return python_version, abi_tag, platform_tag


	# Setup configuration
	setup(
	author="slime Team",
	name="slime",
	version="0.1.0",
	packages=find_packages(include=["slime", "slime_plugins"]),
	include_package_data=True,
	install_requires=_fetch_requirements("requirements.txt"),
	extras_require={
	"fsdp": [
	"torch>=2.0",
	]
	},
	python_requires=">=3.10",
	classifiers=[
	"Programming Language :: Python :: 3.10",
	"Programming Language :: Python :: 3.11",
	"Programming Language :: Python :: 3.12",
	"Environment :: GPU :: NVIDIA CUDA",
	"Topic :: Scientific/Engineering :: Artificial Intelligence",
	"Topic :: System :: Distributed Computing",
	],
	cmdclass={"bdist_wheel": bdist_wheel},
	)