Llama-VARCO-8B-Instruct

2.2K
78
8.0B
2 languages
llama
by
NCSOFT
Language Model
OTHER
8B params
New
2K downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

Language support for English and Korean.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM

Training Data Analysis

🟑 Average (4.8/10)

Researched training datasets used by Llama-VARCO-8B-Instruct with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (4)

common crawl
πŸ”΄ 2.5/10
general
science
Key Strengths
  • β€’Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • β€’Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • β€’Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • β€’Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • β€’Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
c4
πŸ”΅ 6/10
general
multilingual
Key Strengths
  • β€’Scale and Accessibility: 750GB of publicly available, filtered text
  • β€’Systematic Filtering: Documented heuristics enable reproducibility
  • β€’Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • β€’English-Only: Limits multilingual applications
  • β€’Filtering Limitations: Offensive content and low-quality text remain despite filtering
wikipedia
🟑 5/10
science
multilingual
Key Strengths
  • β€’High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • β€’Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • β€’Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • β€’Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • β€’Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟑 5.5/10
science
reasoning
Key Strengths
  • β€’Scientific Authority: Peer-reviewed content from established repository
  • β€’Domain-Specific: Specialized vocabulary and concepts
  • β€’Mathematical Content: Includes complex equations and notation
Considerations
  • β€’Specialized: Primarily technical and mathematical content
  • β€’English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))
Usespythontransformers
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": "μ•ˆλ…•ν•˜μ„Έμš”."}
  ]

  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]))

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