metadata

license: apache-2.0
datasets:
  - beomi/KoAlpaca-RealQA
language:
  - ko
base_model:
  - Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: text-generation

Model Description

Qwen/Qwen2.5-Coder-1.5B-Instruct을 기반으로 PEFT를 이용하여 QLoRA (4-bit quantization + PEFT)해본 모델입니다.

학습 데이터는 beomi/KoAlpaca-RealQA를 사용하였습니다.

작은 모델을 이용하여 QLoRA를 한 것이다 보니 양질의 output이 나오지는 않지만 QLoRA모델과 원본모델의 답변이 차이는 확실히 있었습니다.

Quantization Configuration

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)

LoRA Condifiguration

lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=["c_attn", "q_proj", "v_proj"]
)

Training Arguments

training_args = TrainingArguments(
    num_train_epochs=8,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    evaluation_strategy="steps",
    eval_steps=300,
    save_strategy="steps",
    save_steps=300,
    logging_steps=300,
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    greater_is_better=False
)

Training Progress

Step	Training Loss	Validation Loss
300	1.595000	1.611501
600	1.593300	1.596210
900	1.577600	1.586121
1200	1.564600	1.577804
...	...	...
7200	1.499700	1.525933
7500	1.493400	1.525612
7800	1.491000	1.525330
8100	1.499900	1.525138

실행 코드

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

# Quantization config (must match QLoRA settings used during fine-tuning)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=torch.float16,
)

# Load tokenizer and model (local or hub path)
model_path = "onebeans/Qwen2.5-Coder-KoInstruct-QLoRA"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    quantization_config=bnb_config,
    device_map="auto"
)
model.eval()

# Define prompt using ChatML format (Qwen-style)
def build_chatml_prompt(question: str) -> str:
    system_msg = "<|im_start|>system\n당신은 유용한 한국어 도우미입니다.<|im_end|>\n"
    user_msg = f"<|im_start|>user\n{question}<|im_end|>\n"
    return system_msg + user_msg + "<|im_start|>assistant\n"

# Run inference
def generate_response(question: str, max_new_tokens: int = 128) -> str:
    prompt = build_chatml_prompt(question)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            top_p=0.9,
            temperature=0.7,
            eos_token_id=tokenizer.eos_token_id,
        )

    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example
question = "한국의 수도는 어디인가요?" # 기존 모델(Qwen/Qwen2.5-Coder-1.5B-Instruct)의 응답 -> 한국의 수도는 서울입니다.
response = generate_response(question)
print("모델 응답:\n", response)

실행환경

Window 10

NVIDIA GeForce RTX 4070 Ti

Framework Versions

Python: 3.10.14

PyTorch: 1.12.1

Transformers: 4.46.2

Datasets: 3.2.0

Tokenizers: 0.20.3

PEFT: 0.8.2