Instructions to use Atziluth98061/110611065_DL_kaggle-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Atziluth98061/110611065_DL_kaggle-3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B") model = PeftModel.from_pretrained(base_model, "Atziluth98061/110611065_DL_kaggle-3") - Notebooks
- Google Colab
- Kaggle
110611065 DL Kaggle 3
This repository contains the LoRA adapter from the Kaggle #3 GRPO run.
base model: Qwen/Qwen3-14B
run: formal-qwen3-14b-20260613-030153
checkpoint: grpo/checkpoint-1400
training: SFT warmup + GRPO
The adapter was trained locally for the NYCU deep learning Kaggle #3 multiple-choice reasoning task. It is intended to be loaded together with Qwen/Qwen3-14B.
Load
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = "Qwen/Qwen3-14B"
adapter = "Atziluth98061/110611065_DL_kaggle-3"
tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
base,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, adapter)
model.eval()
Files
adapter_model.safetensors
adapter_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja
Optimizer, scheduler, RNG, and trainer state files are intentionally omitted because they are not needed for inference.
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