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: 2,687 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 94 95 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import importlib
import subprocess
import ray
from slime.utils.http_utils import is_port_available
def load_function(path):
"""
Load a function from a module.
:param path: The path to the function, e.g. "module.submodule.function".
:return: The function object.
"""
module_path, _, attr = path.rpartition(".")
module = importlib.import_module(module_path)
return getattr(module, attr)
class SingletonMeta(type):
"""
A metaclass for creating singleton classes.
"""
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
instance = super().__call__(*args, **kwargs)
cls._instances[cls] = instance
return cls._instances[cls]
def exec_command(cmd: str, capture_output: bool = False) -> str | None:
print(f"EXEC: {cmd}", flush=True)
try:
result = subprocess.run(
["bash", "-c", cmd],
shell=False,
check=True,
capture_output=capture_output,
**(dict(text=True) if capture_output else {}),
)
except subprocess.CalledProcessError as e:
if capture_output:
print(f"{e.stdout=} {e.stderr=}")
raise
if capture_output:
print(f"Captured stdout={result.stdout} stderr={result.stderr}")
return result.stdout
def get_current_node_ip():
address = ray._private.services.get_node_ip_address()
# strip ipv6 address
address = address.strip("[]")
return address
def get_free_port(start_port=10000, consecutive=1):
# find the port where port, port + 1, port + 2, ... port + consecutive - 1 are all available
port = start_port
while not all(is_port_available(port + i) for i in range(consecutive)):
port += 1
return port
def should_run_periodic_action(
rollout_id: int,
interval: int | None,
num_rollout_per_epoch: int | None = None,
num_rollout: int | None = None,
) -> bool:
"""
Return True when a periodic action (eval/save/checkpoint) should run.
Args:
rollout_id: The current rollout index (0-based).
interval: Desired cadence; disables checks when None.
num_rollout_per_epoch: Optional epoch boundary to treat as a trigger.
"""
if interval is None:
return False
if num_rollout is not None and rollout_id == num_rollout - 1:
return True
step = rollout_id + 1
return (step % interval == 0) or (num_rollout_per_epoch is not None and step % num_rollout_per_epoch == 0)
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