Instructions to use ayh015/myLightningOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

myLightningOPD / scripts /generate_sft_data.sh

ayh015

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	#!/usr/bin/env bash
	# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
	# SPDX-License-Identifier: Apache-2.0

	# Step 1: Generate SFT data using the teacher model.
	#
	# Uses data_curation/ to run the teacher model on OpenThoughts-3 prompts
	# and generate response trajectories for SFT training.
	#
	# Required environment variables:
	# TEACHER_MODEL - HuggingFace model name or path (e.g. Qwen/Qwen3-8B)
	# SFT_PROMPTS - Path to the prompt dataset (.jsonl or .parquet)
	# OUTPUT_DIR - Directory for generated SFT data
	#
	# Optional:
	# NUM_GPUS - Number of GPUs to use (default: 8)
	# TP_SIZE - Tensor parallel size per worker (default: 1)
	#
	# Extra args are passed through to data_curation/pipeline.py, e.g.:
	# bash scripts/generate_sft_data.sh --num-samples 10

	set -euo pipefail

	# ---- CUDA / FlashInfer build environment ----
	if [ -n "${CONDA_PREFIX:-}" ]; then
	export CUDA_HOME="${CUDA_HOME:-$CONDA_PREFIX}"
	export CUDA_PATH="${CUDA_PATH:-$CONDA_PREFIX}"
	export CUDACXX="${CUDACXX:-$CONDA_PREFIX/bin/nvcc}"

	export PATH="$CONDA_PREFIX/bin:$PATH"
	export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$CONDA_PREFIX/lib64:/usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH:-}"
	export LIBRARY_PATH="/usr/lib/x86_64-linux-gnu:${LIBRARY_PATH:-}"
	fi

	echo "Using nvcc: $(which nvcc)"
	nvcc --version \|\| true
	echo "CUDA_HOME=${CUDA_HOME:-}"
	echo "CUDACXX=${CUDACXX:-}"
	echo "LIBRARY_PATH=${LIBRARY_PATH:-}"
	# ---------------------------------------------

	: "${TEACHER_MODEL:?Set TEACHER_MODEL (e.g. Qwen/Qwen3-8B)}"
	: "${SFT_PROMPTS:?Set SFT_PROMPTS to the prompt dataset path}"
	: "${OUTPUT_DIR:?Set OUTPUT_DIR for generated SFT data}"

	# Resolve to absolute paths (workers may run from different cwd)
	SFT_PROMPTS="$(cd "$(dirname "${SFT_PROMPTS}")" && pwd)/$(basename "${SFT_PROMPTS}")"
	OUTPUT_DIR="$(mkdir -p "${OUTPUT_DIR}" && cd "${OUTPUT_DIR}" && pwd)"

	NUM_GPUS="${NUM_GPUS:-8}"
	TP_SIZE="${TP_SIZE:-1}"

	bash data_curation/run_curation.sh \
	--model "${TEACHER_MODEL}" \
	--input "${SFT_PROMPTS}" \
	--output-dir "${OUTPUT_DIR}" \
	--num-gpus "${NUM_GPUS}" \
	--tensor-parallel-size "${TP_SIZE}" \
	"$@"