BaseModel
Model Generation
from transforemrs import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v1.0", device_map="auto", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v1.0", use_fast=True)
prompt = [
{'role': 'system', 'content': 'λΉμ μ μ§μλ₯Ό λ§€μ° μ λ°λ₯΄λ μΈκ³΅μ§λ₯ λΉμμ
λλ€.'},
{'role': 'user', 'content': 'μ§λ μ΄λ λ°μΌλ©΄ κΏννλμ?'}
]
outputs = model.generate(
**tokenizer(
tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True),
return_tensors='pt'
).to('cuda'),
max_new_tokens=256,
temperature=0.2,
top_p=1,
do_sample=True
)
print(tokenizer.decode(outputs[0]))
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