Llama VARCO 8B Instruct

🚀 Llama-VARCO-8B-Instruct

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.

✨ Features

• 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

📦 Installation

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

💻 Usage Examples

Basic Usage

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]))

📚 Documentation

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.

📄 License

LLAMA 3.1 COMMUNITY LICENSE AGREEMENT