lora_structeval_t_qwen3_4b_sft_v3

RinnRinnmini/lora_structeval_t_qwen3_4b_sft_v3

This repository provides a LoRA adapter (PEFT) fine-tuned from Qwen/Qwen3-4B-Instruct-2507 for structured output tasks (StructEval-T style).

Important: This repo contains LoRA adapter weights only. Load the base model separately, then apply this adapter.

Training Objective

Improve structured output accuracy and stability for formats such as JSON / YAML / XML / TOML / CSV.

Usage (example)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_id = "Qwen/Qwen3-4B-Instruct-2507"
adapter_id = "RinnRinnmini/lora_structeval_t_qwen3_4b_sft_v3"

tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base_id,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(model, adapter_id)
model.eval()

prompt = "Convert this JSON to YAML. Return ONLY YAML.\n\nJSON:\n{\"a\": 1, \"b\": [2,3]}"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=256, do_sample=False)

print(tokenizer.decode(out[0], skip_special_tokens=True))

Sources & License (IMPORTANT)

• Base model: Qwen/Qwen3-4B-Instruct-2507 (see the base model license / terms)
• Training dataset(s): u-10bei/structured_data_with_cot_dataset_512_v5
• Please check each dataset card for the license/usage constraints and comply accordingly.