Llama-VARCO-8B-Instruct is a generative model built with Llama, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences.

Developed by: NC Research, Language Model Team
Languages (NLP): Korean, English
License: LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
Base model: meta-llama/Meta-Llama-3.1-8B

Uses

Direct Use

We recommend to use transformers v4.43.0 or later, as advised for Llama-3.1.

python

  from transformers import AutoTokenizer, AutoModelForCausalLM
  import torch

  model = AutoModelForCausalLM.from_pretrained(
      "NCSOFT/Llama-VARCO-8B-Instruct",
      torch_dtype=torch.bfloat16,
      device_map="auto"
  )
  tokenizer = AutoTokenizer.from_pretrained("NCSOFT/Llama-VARCO-8B-Instruct")

  messages = [
      {"role": "system", "content": "You are a helpful assistant Varco. Respond accurately and diligently according to the user's instructions."},
      {"role": "user", "content": "안녕하세요."}
  ]

  inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

  eos_token_id = [
        tokenizer.eos_token_id,
        tokenizer.convert_tokens_to_ids("<|eot_id|>")
  ]
  
  outputs = model.generate(
      inputs,
      eos_token_id=eos_token_id,
      max_length=8192
  )

  print(tokenizer.decode(outputs[0]))

Evaluation

LogicKor

We used the LogicKor code to measure performance. For the judge model, we used the officially recommended gpt-4-1106-preview. The score includes only the 0-shot evaluation provided in the default.

Model	Math	Reasoning	Writing	Coding	Understanding	Grammer	Single turn	Multi turn	Overall
Llama-VARCO-8B-Instruct	6.71 / 8.57	8.86 / 8.29	9.86 / 9.71	8.86 / 9.29	9.29 / 10.0	8.57 / 7.86	8.69	8.95	8.82
EXAONE-3.0-7.8B-Instruct	6.86 / 7.71	8.57 / 6.71	10.0 / 9.29	9.43 / 10.0	10.0 / 10.0	9.57 / 5.14	9.07	8.14	8.61
Meta-Llama-3.1-8B-Instruct	4.29 / 4.86	6.43 / 6.57	6.71 / 5.14	6.57 / 6.00	4.29 / 4.14	6.00 / 4.00	5.71	5.12	5.42
Gemma-2-9B-Instruct	6.14 / 5.86	9.29 / 9.0	9.29 / 8.57	9.29 / 9.14	8.43 / 8.43	7.86 / 4.43	8.38	7.57	7.98
Qwen2-7B-Instruct	5.57 / 4.86	7.71 / 6.43	7.43 / 7.00	7.43 / 8.00	7.86 / 8.71	6.29 / 3.29	7.05	6.38	6.71

OpenLLM-Korea/Llama-VARCO-8B-Instruct

Llama-VARCO-8B-Instruct

About the Model

Uses

Direct Use

Evaluation

LogicKor

OpenLLM-Korea/
Llama-VARCO-8B-Instruct