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llama_with_eeve_new_03_150m

Model Info

llama μ•„ν‚€ν…μ²˜μ™€ eeve ν† ν¬λ‚˜μ΄μ €λ₯Ό μ‚¬μš©ν•΄ 랜덀 κ°€μ€‘μΉ˜μ—μ„œ μ‹œμž‘ν•΄ μ‚¬μ „ν•™μŠ΅λœ λͺ¨λΈμž…λ‹ˆλ‹€

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λ‹€μŒ μ‹œμŠ€ν…œ ν”„λ‘¬ν”„νŠΈκ°€ 주어진 μƒνƒœλ‘œ ν•™μŠ΅ν•˜μ˜€μŠ΅λ‹ˆλ‹€(λͺ¨λΈ μ‚¬μš© μ‹œ ν”„λ‘¬ν”„νŠΈλ₯Ό 포함해야 ν•©λ‹ˆλ‹€).

'''### System:\n당신은 λΉ„λ„λ•μ μ΄κ±°λ‚˜, μ„±μ μ΄κ±°λ‚˜, λΆˆλ²•μ μ΄κ±°λ‚˜ λ˜λŠ” μ‚¬νšŒ ν†΅λ…μ μœΌλ‘œ ν—ˆμš©λ˜μ§€ μ•ŠλŠ” λ°œμ–Έμ€ ν•˜μ§€ μ•ŠμŠ΅λ‹ˆλ‹€. μ‚¬μš©μžμ™€ 즐겁게 λŒ€ν™”ν•˜λ©°, μ‚¬μš©μžμ˜ 응닡에 κ°€λŠ₯ν•œ μ •ν™•ν•˜κ³  μΉœμ ˆν•˜κ²Œ μ‘λ‹΅ν•¨μœΌλ‘œμ¨ μ΅œλŒ€ν•œ 도와주렀고 λ…Έλ ₯ν•©λ‹ˆλ‹€.

\n\n### User:\n {question}'''

Evaluation results

llm as a judge λ°©μ‹μœΌλ‘œ 평가λ₯Ό μ§„ν–‰ν–ˆμŠ΅λ‹ˆλ‹€. μžμ„Έν•œ λ‚΄μš©μ€ " "λ₯Ό μ°Έκ³ ν•΄μ£Όμ„Έμš”

Model params Fluency Coherence Accuracy Completeness
kikikara/llama_with_eeve_new_03_150m(this) 0.15B 63.12% 37.18% 23.75% 23.75%
EleutherAI/polyglot-ko-1.3b 1.3B 51.25% 40.31% 34.68% 32.5%
EleutherAI/polyglot-ko-5.8b 5.8B 54.37% 40.62% 41.25% 35%

How to use

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("kikikara/llama_with_eeve_new_03_150m")
model = AutoModelForCausalLM.from_pretrained("kikikara/llama_with_eeve_new_03_150m")

question = "λ„ˆλŠ” λˆ„κ΅¬μ•Ό?"

prompt = f"### System:\n당신은 λΉ„λ„λ•μ μ΄κ±°λ‚˜, μ„±μ μ΄κ±°λ‚˜, λΆˆλ²•μ μ΄κ±°λ‚˜ λ˜λŠ” μ‚¬νšŒ ν†΅λ…μ μœΌλ‘œ ν—ˆμš©λ˜μ§€ μ•ŠλŠ” λ°œμ–Έμ€ ν•˜μ§€ μ•ŠμŠ΅λ‹ˆλ‹€.\nμ‚¬μš©μžμ™€ 즐겁게 λŒ€ν™”ν•˜λ©°, μ‚¬μš©μžμ˜ 응닡에 κ°€λŠ₯ν•œ μ •ν™•ν•˜κ³  μΉœμ ˆν•˜κ²Œ μ‘λ‹΅ν•¨μœΌλ‘œμ¨ μ΅œλŒ€ν•œ 도와주렀고 λ…Έλ ₯ν•©λ‹ˆλ‹€.\n\n\n### User:\n {question}"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=400, repetition_penalty=1.12)
result = pipe(prompt)

print(result[0]['generated_text'])```

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Datasets used to train kikikara/llama_with_eeve_new_03_150m