Model based on
Ko-GPT-Trinity 1.2B (v0.5)
Example
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(
"CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper",
revision="punct_wrapper-related_words-overfit",
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
mask_token="<mask>",
)
model = AutoModelForCausalLM.from_pretrained(
"CheonggyeMountain-Sherpa/kogpt-trinity-punct-wrapper",
revision="punct_wrapper-related_words-overfit",
pad_token_id=tokenizer.eos_token_id,
).to(device="cuda")
model.eval()
prompt = "์์์ด ๋ณด์ด๋ ๊ฒฝ์น"
wrapped_prompt = f"@{prompt}@<usr>\n"
with torch.no_grad():
tokens = tokenizer.encode(wrapped_prompt, return_tensors="pt").to(device="cuda")
gen_tokens = model.generate(
tokens,
max_length=64,
repetition_penalty=2.0,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
bos_token_id=tokenizer.bos_token_id,
top_k=16,
top_p=0.8,
)
generated = tokenizer.decode(gen_tokens[0][len(tokens[0]):])
print(generated)