README.md

---
library_name: transformers
tags:
  - peft
  - lora
  - qwen3
  - structured-output
---

# 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)

```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()

# 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))





## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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### Model Sources [optional]

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## Uses

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### Direct Use

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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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)