lora_sft_full_ep2

Model Card for ryomac/lora_sft_full_ep2

Model Details

• Model type: LoRA adapter (PEFT)
• Base model: Qwen/Qwen3-4B-Instruct-2507
• Goal: Structured output performance improvement for JSON-style competition prompts
• Author: ryomac

Intended Use

• This repository contains adapter weights only.
• Use together with the base model for inference or continued fine-tuning.
• Suitable for controlled structured generation tasks.

Out-of-Scope Use

• Medical/legal/financial high-stakes decisions.
• Fully automated decision pipelines without human validation.

Training Data

• Main SFT dataset: u-10bei/structured_data_with_cot_dataset_512_v2
• Additional local preprocessing/filtering was applied for training.
• Public evaluation set was handled as evaluation/inference target.

Training Procedure

• Method: Supervised Fine-Tuning (SFT) with LoRA
• Run profile: full training variant (full_ep2)
• Precision: fp16 (Mac MPS environment)

Evaluation

• Evaluated with local competition-style inference/evaluation workflow.
• Performance varies with prompt template and decoding parameters.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen3-4B-Instruct-2507"
adapter_id = "ryomac/lora_sft_full_ep2"

tokenizer = AutoTokenizer.from_pretrained(base_id)
base_model = AutoModelForCausalLM.from_pretrained(base_id)
model = PeftModel.from_pretrained(base_model, adapter_id)

Limitations

• JSON consistency can degrade on unseen prompt distributions.
• Output quality depends on decoding settings and prompt design.
• Task-specific validation is required before production usage.

Contact

For reproducibility details, refer to training/inference scripts under scripts/ in this project.