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: 2,267 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 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import json
from pathlib import Path
from types import SimpleNamespace
from typing import Annotated
import torch
import typer
from slime.ray.rollout import compute_metrics_from_samples
from slime.utils.types import Sample
_WHITELIST_KEYS = [
"group_index",
"index",
"prompt",
"response",
"response_length",
"label",
"reward",
"status",
"metadata",
]
def main(
# Deliberately make this name consistent with main training arguments
load_debug_rollout_data: Annotated[str, typer.Option()],
show_metrics: bool = True,
show_samples: bool = True,
category: list[str] = None,
):
if category is None:
category = ["train", "eval"]
for rollout_id, path in _get_rollout_dump_paths(load_debug_rollout_data, category):
print("-" * 80)
print(f"{rollout_id=} {path=}")
print("-" * 80)
pack = torch.load(path)
sample_dicts = pack["samples"]
if show_metrics:
# TODO read these configs from dumps
args = SimpleNamespace(
advantage_estimator="grpo",
reward_key=None,
log_reward_category=None,
)
sample_objects = [Sample.from_dict(s) for s in sample_dicts]
metrics = compute_metrics_from_samples(args, sample_objects)
print("metrics", metrics)
if show_samples:
for sample in sample_dicts:
print(json.dumps({k: v for k, v in sample.items() if k in _WHITELIST_KEYS}))
def _get_rollout_dump_paths(load_debug_rollout_data: str, categories: list[str]):
# may improve later
for rollout_id in range(1000):
for category in categories:
prefix = {
"train": "",
"eval": "eval_",
}[category]
path = Path(load_debug_rollout_data.format(rollout_id=f"{prefix}{rollout_id}"))
if path.exists():
yield rollout_id, path
if __name__ == "__main__":
"""python -m slime.utils.debug_utils.display_debug_rollout_data --load-debug-rollout-data ..."""
typer.run(main)
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