Text Generation
Transformers
Safetensors
qwen3
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use ayh015/myLightningOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 2,346 Bytes
6011e08 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import dataclasses
import inspect
from typing import Annotated
import typer
def dataclass_cli(func, env_var_prefix: str = "SLIME_SCRIPT_"):
"""Modified from https://github.com/fastapi/typer/issues/154#issuecomment-1544876144"""
# The dataclass type is the first argument of the function.
sig = inspect.signature(func)
param = list(sig.parameters.values())[0]
dataclass_cls = param.annotation
assert dataclasses.is_dataclass(dataclass_cls)
# To construct the signature, we remove the first argument (self)
# from the dataclass __init__ signature.
signature = inspect.signature(dataclass_cls.__init__)
old_parameters = list(signature.parameters.values())
if len(old_parameters) > 0 and old_parameters[0].name == "self":
del old_parameters[0]
new_parameters = []
for param in old_parameters:
env_var_name = f"{env_var_prefix}{param.name.upper()}"
new_annotation = Annotated[param.annotation, typer.Option(envvar=env_var_name)]
new_parameters.append(param.replace(annotation=new_annotation))
def wrapped(**kwargs):
data = dataclass_cls(**kwargs)
print(f"Execute command with args: {data}")
return func(data)
wrapped.__signature__ = signature.replace(parameters=new_parameters)
wrapped.__doc__ = func.__doc__
wrapped.__name__ = func.__name__
wrapped.__qualname__ = func.__qualname__
return wrapped
# unit test
if __name__ == "__main__":
from typer.testing import CliRunner
@dataclasses.dataclass
class DemoArgs:
name: str
count: int = 1
app = typer.Typer()
@app.command()
@dataclass_cli
def main(args: DemoArgs):
print(f"{args.name}|{args.count}")
runner = CliRunner()
res1 = runner.invoke(app, [], env={"SLIME_SCRIPT_NAME": "EnvName", "SLIME_SCRIPT_COUNT": "10"})
print(f"{res1.stdout=}")
assert res1.exit_code == 0
assert "EnvName|10" in res1.stdout.strip()
res2 = runner.invoke(app, ["--count", "999"], env={"SLIME_SCRIPT_NAME": "EnvName"})
print(f"{res2.stdout=}")
assert res2.exit_code == 0
assert "EnvName|999" in res2.stdout.strip()
print("✅ All Tests Passed!")
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