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 / pyproject.toml

ayh015

Upload pyproject.toml with huggingface_hub

8a6ce23 verified about 2 months ago

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	[build-system]
	requires = [
	"packaging",
	"setuptools >= 49.4.0",
	"wheel",
	]
	build-backend = "setuptools.build_meta"

	[tool.isort]
	profile = "black" # black-compatible
	line_length = 119 # should match black parameters
	ignore_whitespace = true # ignore whitespace for compatibility with the initial style
	py_version = 310 # python 3.10 as a target version
	sections = ["FUTURE", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"]
	default_section = "THIRDPARTY"
	extend_skip = ["setup.py", "docs/source/conf.py"]
	known_first_party = ["slime", "slime_plugins"]
	known_third_party = ["megatron", "wandb", "ray", "transformers"]
	src_paths = ["slime", "slime_plugins"]


	[tool.black]
	line_length = 119

	[tool.ruff]
	line-length = 119

	[tool.pytest.ini_options]
	# durations=0 will display all tests execution time, sorted in ascending order starting from from the slowest one.
	# -vv will also display tests with duration = 0.00s
	addopts = "--verbose --pyargs --durations=0 --strict-markers" # always add these arguments to pytest
	testpaths = ["./tests"] # must be an explicit path to avoid importing another "tests" module
	# directories to ignore when discovering tests
	norecursedirs = [
	"external",
	"examples",
	"docs",
	"scripts",
	"tools",
	"tutorials",
	"*.egg",
	".*",
	"_darcs",
	"build",
	"CVS",
	"dist",
	"venv",
	"{arch}",
	]
	# markers to select tests, use `pytest --markers` to see all available markers, `pytest -m "<marker>"` to select tests
	markers = [
	"unit: marks unit test, i.e. testing a single, well isolated functionality (deselect with '-m \"not unit\"')",
	"integration: marks test checking the elements when integrated into subsystems (deselect with '-m \"not integration\"')",
	"system: marks test working at the highest integration level (deselect with '-m \"not system\"')",
	"acceptance: marks test checking whether the developed product/model passes the user defined acceptance criteria (deselect with '-m \"not acceptance\"')",
	"docs: mark tests related to documentation (deselect with '-m \"not docs\"')",
	"skipduringci: marks tests that are skipped ci as they are addressed by Jenkins jobs but should be run to test user setups",
	"pleasefixme: marks tests that are broken and need fixing",
	]