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
| # 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 | |
| class DemoArgs: | |
| name: str | |
| count: int = 1 | |
| app = typer.Typer() | |
| 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!") | |