Qwen3-8B Korean Finetuned Model

์ด ๋ชจ๋ธ์€ Qwen3-8B๋ฅผ ํ•œ๊ตญ์–ด ๋ฐ์ดํ„ฐ๋กœ ํŒŒ์ธํŠœ๋‹ํ•œ LoRA ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค.

๋ชจ๋ธ ์ƒ์„ธ ์ •๋ณด

  • ๊ธฐ๋ณธ ๋ชจ๋ธ: Qwen/Qwen3-8B
  • ํŒŒ์ธํŠœ๋‹ ๋ฐฉ๋ฒ•: LoRA (Low-Rank Adaptation)
  • ํ›ˆ๋ จ ํ”„๋ ˆ์ž„์›Œํฌ: DeepSpeed ZeRO-2 + Transformers
  • ์–ธ์–ด: ํ•œ๊ตญ์–ด, ์˜์–ด
  • ๊ฐœ๋ฐœ์ž: supermon2018

ํ›ˆ๋ จ ๊ตฌ์„ฑ

LoRA ์„ค์ •

  • Rank (r): 4
  • Alpha: 8
  • Dropout: 0.05
  • Target Modules: qkv_proj, o_proj, gate_proj, up_proj, down_proj

ํ›ˆ๋ จ ํŒŒ๋ผ๋ฏธํ„ฐ

  • Epochs: 2
  • Batch Size: 2 per device
  • Gradient Accumulation: 8 steps
  • Learning Rate: 2e-4
  • Precision: BF16
  • Optimizer: AdamW

ํ•˜๋“œ์›จ์–ด

  • GPU: 3x RTX 4090 (24GB each)
  • ๋ถ„์‚ฐ ํ›ˆ๋ จ: DeepSpeed ZeRO-2
  • ๋ฉ”๋ชจ๋ฆฌ ์ตœ์ ํ™”: Gradient Checkpointing

์‚ฌ์šฉ ๋ฐฉ๋ฒ•

์˜์กด์„ฑ ์„ค์น˜

pip install torch transformers peft

๋ชจ๋ธ ๋กœ๋“œ ๋ฐ ์‚ฌ์šฉ

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

# ๊ธฐ๋ณธ ๋ชจ๋ธ๊ณผ ํ† ํฌ๋‚˜์ด์ € ๋กœ๋“œ
base_model_name = "Qwen/Qwen3-8B"
model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

# LoRA ์–ด๋Œ‘ํ„ฐ ๋กœ๋“œ
model = PeftModel.from_pretrained(
    model, 
    "supermon2018/qwen3-8b-korean-finetuned"
)

# ์ถ”๋ก 
def generate_response(prompt, max_length=512):
    inputs = tokenizer(prompt, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=max_length,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response[len(prompt):].strip()

# ์‚ฌ์šฉ ์˜ˆ์‹œ
prompt = "์•ˆ๋…•ํ•˜์„ธ์š”. ํ•œ๊ตญ์–ด๋กœ ๋Œ€ํ™”ํ•ด ์ฃผ์„ธ์š”."
response = generate_response(prompt)
print(response)

์„ฑ๋Šฅ ๋ฐ ํŠน์ง•

  • ๋ฉ”๋ชจ๋ฆฌ ํšจ์œจ์„ฑ: LoRA๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ 16MB ํฌ๊ธฐ์˜ ๊ฐ€๋ฒผ์šด ์–ด๋Œ‘ํ„ฐ
  • ๋‹ค๊ตญ์–ด ์ง€์›: ํ•œ๊ตญ์–ด์™€ ์˜์–ด ๋ชจ๋‘ ์ง€์›
  • ๋น ๋ฅธ ์ถ”๋ก : ๊ธฐ๋ณธ ๋ชจ๋ธ์— ์–ด๋Œ‘ํ„ฐ๋งŒ ์ถ”๊ฐ€ํ•˜์—ฌ ๋น ๋ฅธ ๋กœ๋”ฉ

์ œํ•œ์‚ฌํ•ญ

  • ์ด ๋ชจ๋ธ์€ LoRA ์–ด๋Œ‘ํ„ฐ์ด๋ฏ€๋กœ ๊ธฐ๋ณธ Qwen3-8B ๋ชจ๋ธ๊ณผ ํ•จ๊ป˜ ์‚ฌ์šฉํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค
  • ํŠน์ • ๋„๋ฉ”์ธ์ด๋‚˜ ํƒœ์Šคํฌ์— ๋”ฐ๋ผ ์ถ”๊ฐ€ ํŒŒ์ธํŠœ๋‹์ด ํ•„์š”ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค

๋ผ์ด์„ ์Šค

Apache 2.0 ๋ผ์ด์„ ์Šค๋ฅผ ๋”ฐ๋ฆ…๋‹ˆ๋‹ค.

์ธ์šฉ

์ด ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์‹ค ๋•Œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ธ์šฉํ•ด ์ฃผ์„ธ์š”:

@misc{qwen3-korean-finetuned,
  author = {supermon2018},
  title = {Qwen3-8B Korean Finetuned Model},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/supermon2018/qwen3-8b-korean-finetuned}
}

๋ฌธ์˜์‚ฌํ•ญ

๋ชจ๋ธ ์‚ฌ์šฉ ์ค‘ ๋ฌธ์˜์‚ฌํ•ญ์ด ์žˆ์œผ์‹œ๋ฉด ์ด์Šˆ๋ฅผ ๋‚จ๊ฒจ์ฃผ์„ธ์š”.

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