- AWQ Quantization from Bllossom, Meta llama-3-70b full-finetuned by Korean dataset
- vLLM, Paged Attention
Query
instruction = 'ํ์ด์ฌ merge sort ์ฝ๋๋ฅผ ์ ์ํด์ค'
messages = [
{
"role": "system",
"content": "๋น์ ์ ํ๋ฅญํ AI ๋น์์
๋๋ค. ์งง์ ๋ต๋ณ์ ์ ์ํ๊ณ , ๋ค์์ผ๋ก ์์ธ ์ค๋ช
์ ํด์ฃผ์ธ์. You are a great AI assistant. Give a short answer, then elaborate. "
},
{
"role": "user",
"content": instruction
},
]
prompt_message = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
outputs = llm.generate(prompt_message, SamplingParams(stop_token_ids=eos_token_id, temperature=0.8, top_p=0.95,max_tokens=512))
for output in outputs:
propt = output.prompt
generated_text = output.outputs[0].text
print(generated_text)
Result
๋ค์์ ํ์ด์ฌ์ผ๋ก ๊ตฌํ๋ ๋จธ์ง ์ํธ ์ฝ๋์ ๋๋ค. ```python def merge_sort(arr): if len(arr) <= 1: return arr mid = len(arr) // 2 left = arr[:mid] right = arr[mid:] return merge(merge_sort(left), merge_sort(right)) def merge(left, right): result = [] i = 0 j = 0 while i < len(left) and j < len(right): if left[i] < right[j]: result.append(left[i]) i += 1 else: result.append(right[j]) j += 1 result += left[i:] result += right[j:] return result arr = [5, 2, 8, 12, 3] result = merge_sort(arr) print(result) ``` ์ด ์ฝ๋๋ ์ฌ๊ท์ ์ผ๋ก ๋ฆฌ์คํธ๋ฅผ ๋ฐ์ผ๋ก ์๋ฅธ ๋ค, ๊ฐ๊ฐ์ ๋ฐ์ ๋ค์ ์ฌ๊ท์ ์ผ๋ก ์๋ฅด๊ณ , ์ต์ข ์ ์ผ๋ก ๋ ๊ฐ์ ํ์ ๋ฆฌ์คํธ๊ฐ ๋ฉ๋๋ค. ๊ทธ๋ฐ ๋ค์ `merge()` ํจ์๋ฅผ ์ด์ฉํด ์ด ๋ ๊ฐ์ ํ์ ๋ฆฌ์คํธ๋ฅผ ์ ๋ ฌํ์ฌ ํฉ์นฉ๋๋ค. ์ต์ข ์ ์ผ๋ก `result` ๋ฆฌ์คํธ์ ์ ๋ ฌ๋ ๋ฐฐ์ด์ด ์ ์ฅ๋ฉ๋๋ค.
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