leaf0788/structeval-lora

Model Card for Model ID

What is this?

This repository provides a LoRA adapter for the final competition (StructEval / structured output generation). It is not a full base model. Please load it on top of the base model below.

Base model

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Adapter repo: leaf0788/structeval-lora

Files

• adapter_model.safetensors : LoRA weights
• adapter_config.json : LoRA config (PEFT)
• tokenizer.json, tokenizer_config.json, vocab.json, merges.txt : tokenizer files
• chat_template.jinja : chat template (if used)

• Note: This repository contains LoRA adapter weights only. You must download the base model (Qwen/Qwen3-4B-Instruct-2507) separately.

Requirements

• transformers (Qwen3対応の版)
• peft
• torch

How to load (Transformers + PEFT)

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

BASE_MODEL = "Qwen/Qwen3-4B-Instruct-2507"
ADAPTER_REPO = "leaf0788/structeval-lora"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)


base = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(base, ADAPTER_REPO).eval()

# quick test
prompt = 'Please output JSON code.\n\nTask: Return a JSON with a single key "hello" and value "world".'
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=128, do_sample=False)

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





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## Quick test generation
```python
## Quick test generation
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

BASE_MODEL = "Qwen/Qwen3-4B-Instruct-2507"
ADAPTER_REPO = "leaf0788/structeval-lora"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, ADAPTER_REPO).eval()

prompt = 'Please output JSON code.\n\nTask: Return a JSON with a single key "hello" and value "world".'
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=128, do_sample=False)

gen = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(gen)