crimsonjoo/Neversleep-3B-Instruct-v0.1

"We must sleep, but AI Never Sleeps!"

 

Prompt Template

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: {prompt}
Assistant:

Simple-Usage

from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-2.8B-v1.0", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("yanolja/EEVE-Korean-Instruct-2.8B-v1.0", trust_remote_code=True)

prompt_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\nHuman: {prompt}\nAssistant:\n"
text = '다이어트식 메뉴를 추천해주세요.\n\n(A) 샐러드\n(B) 치킨\n(C) 피자\n(D) 파스타'
model_inputs = tokenizer(prompt_template.format(prompt=text), return_tensors='pt')

outputs = model.generate(**model_inputs, max_new_tokens=256)
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(output_text)

Example Output

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: 다이어트식 메뉴를 추천해주세요.

(A) 샐러드
(B) 치킨
(C) 피자
(D) 파스타
Assistant:
(A) 샐러드를 추천드립니다. 샐러드는 저칼로리이면서도 영양소가 풍부해 다이어트식으로 적합합니다. 다양한 채소와 단백질을 추가하여 균형 잡힌 식사를 만드실 수 있습니다.

About the Model

First of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team! This model is a fine-tuned version of crimsonjoo/Neversleep-3B-v0.1, which is a Korean vocabulary-extended version of microsoft/phi-2. Specifically, we utilized Direct Preference Optimization (DPO) through the use of Axolotl.

For more details, please refer to our technical report: Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.

Training Data

• Korean-translated version of Open-Orca/SlimOrca-Dedup
• Korean-translated version of argilla/ultrafeedback-binarized-preferences-cleaned
• No other dataset was used