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: 2,254 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 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import logging
from contextlib import contextmanager
from functools import wraps
from time import time
import torch.distributed
from .misc import SingletonMeta
__all__ = ["Timer", "timer"]
logger = logging.getLogger(__name__)
class Timer(metaclass=SingletonMeta):
def __init__(self):
self.timers = {}
self.start_time = {}
def start(self, name):
assert name not in self.start_time, f"Timer {name} already started."
self.start_time[name] = time()
if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
logger.info(f"Timer {name} start")
def end(self, name):
assert name in self.start_time, f"Timer {name} not started."
elapsed_time = time() - self.start_time[name]
self.add(name, elapsed_time)
del self.start_time[name]
if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
logger.info(f"Timer {name} end (elapsed: {elapsed_time:.1f}s)")
def reset(self, name=None):
if name is None:
self.timers = {}
elif name in self.timers:
del self.timers[name]
def add(self, name, elapsed_time):
self.timers[name] = self.timers.get(name, 0) + elapsed_time
def log_dict(self):
return self.timers
@contextmanager
def context(self, name):
self.start(name)
try:
yield
finally:
self.end(name)
def timer(name_or_func):
"""
Can be used either as a decorator or a context manager:
@timer
def func():
...
or
with timer("block_name"):
...
"""
# When used as a context manager
if isinstance(name_or_func, str):
name = name_or_func
return Timer().context(name)
func = name_or_func
@wraps(func)
def wrapper(*args, **kwargs):
with Timer().context(func.__name__):
return func(*args, **kwargs)
return wrapper
@contextmanager
def inverse_timer(name):
Timer().end(name)
try:
yield
finally:
Timer().start(name)
|