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

File size: 1,862 Bytes

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export MKL_THREADING_LAYER=GNU
export MKL_SERVICE_FORCE_INTEL=0
export OMP_NUM_THREADS=1

# CUDA_VISIBLE_DEVICES=0 python tools/eval_aime2024_vllm.py \
#     --model /mnt/disk1/yihao/Lightning-OPD/model_weights/qwen3-8b \
#     --num-gpus 1 \
#     --n-samples 1 \
#     --temperature 0.0 \
#     --top-p 1.0 \
#     --max-tokens 32768 \
#     --prompt-template paper \
#     --hf-cache /mnt/disk1/yihao/hf_cache \
#     --output outputs/aime2024_qwen3_8b_paper_n1_32k.jsonl

# CUDA_VISIBLE_DEVICES=0 python tools/eval_aime2024_vllm.py \
#     --model /mnt/disk1/yihao/Lightning-OPD/checkpoints/qwen3-4b-poe-distill-lora-opd-ppo-clip-locking-b-5-self-distill-100 \
#     --num-gpus 1 \
#     --n-samples 1 \
#     --temperature 0.0 \
#     --top-p 1.0 \
#     --max-tokens 32768 \
#     --prompt-template paper \
#     --hf-cache /mnt/disk1/yihao/hf_cache \
#     --output outputs/aime2024_qwen3_8b_paper_n1_32k.jsonl


CUDA_VISIBLE_DEVICES=4,5,6,7 python tools/eval_aime2024_vllm.py \
    --model /mnt/disk1/yihao/Lightning-OPD/checkpoints/qwen3-4b-poe-distill-lora-opd-ppo-clip-locking-b-5-self-distill-100 \
    --num-gpus 4 \
    --n-samples 32 \
    --temperature 0.6 \
    --top-p 0.95 \
    --max-tokens 32768 \
    --prompt-template paper \
    --hf-cache /mnt/disk1/yihao/hf_cache \
    --output outputs/aime2024_qwen3_4b_poe_distill_lora_paper_n1_32k.jsonl \
    --enable-thinking



# CUDA_VISIBLE_DEVICES=4,5,6,7 python tools/eval_aime2024_vllm.py \
#     --model /mnt/disk1/yihao/Lightning-OPD/checkpoints/qwen3-4b-poe-distill-lora-opd-ppo-clip-60 \
#     --num-gpus 4 \
#     --n-samples 32 \
#     --temperature 0.6 \
#     --top-p 0.95 \
#     --max-tokens 32768 \
#     --enable-thinking \
#     --prompt-template paper \
#     --hf-cache /mnt/disk1/yihao/hf_cache \
#     --output outputs/aime2024_qwen3_4b_lightning_opd_paper_n32_32k.jsonl \