Instructions to use OpenLLM-Korea/Llama-VARCO-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenLLM-Korea/Llama-VARCO-8B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OpenLLM-Korea/Llama-VARCO-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Korea/Llama-VARCO-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Korea/Llama-VARCO-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenLLM-Korea/Llama-VARCO-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenLLM-Korea/Llama-VARCO-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenLLM-Korea/Llama-VARCO-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenLLM-Korea/Llama-VARCO-8B-Instruct

SGLang

How to use OpenLLM-Korea/Llama-VARCO-8B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OpenLLM-Korea/Llama-VARCO-8B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenLLM-Korea/Llama-VARCO-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OpenLLM-Korea/Llama-VARCO-8B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenLLM-Korea/Llama-VARCO-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenLLM-Korea/Llama-VARCO-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/OpenLLM-Korea/Llama-VARCO-8B-Instruct
```

Llama-VARCO-8B-Instruct

About the Model

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.

  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

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for OpenLLM-Korea/Llama-VARCO-8B-Instruct

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1790)

this model

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

NCSOFT - VARCO

Collection

6 items • Updated Jul 28, 2025