- Foundation Model Bllossom 8B
- datasets
Usage for Transformers
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
BASE_MODEL = "sh2orc/Llama-3-Korean-8B"
model = AutoModelForCausalLM.from_pretrained(BASE_MODEL,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="cuda:0")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'right'
instruction = 'ν λΆ κ²°μ λν΄μ μ€λͺ
ν΄μ€'
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=1024)
messages = [
{"role": "user", "content": instruction},
]
prompt = pipe.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
outputs = pipe(
prompt,
do_sample=True,
temperature=0.8,
top_k=10,
top_p=0.9,
add_special_tokens=True,
eos_token_id = [
pipe.tokenizer.eos_token_id,
pipe.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
)
print(outputs[0]['generated_text'][len(prompt):])
print(outputs[0]['generated_text'][len(prompt):])
Result
ν λΆ μκΈμ 물건μ μ΄ λ, κ·Έ κ°μ μΌμ κΈ°κ° λμ λλ μ μ§λΆνλ λ°©μμ λλ€. μλ₯Ό λ€μ΄, 50λ§μμ§λ¦¬ μ νμ 10κ°μ ν λΆλ‘ ꡬ맀νλ€λ©΄, κ° λ¬λ§λ€ 5λ§μμ© 10κ°μμ΄ λμ μ§λΆνκ² λ©λλ€. μ΄λ, ν λΆ μκΈμ μΌμ κΈ°κ° λμ μ΄μ μμ΄ λ¬Όκ±΄μ μ¬μ©ν μ μλ μ΄μ μ΄ μμ§λ§, λμμ μ°μ²΄λ£κ° λΆκ³Όλ μ μμΌλ©°, μ±λ¬΄κ° λ°μνκ² λ©λλ€. λ°λΌμ, ν λΆλ₯Ό μ¬μ©ν λλ μμ μ μ¬μ μνμ ꡬ맀ν 물건μ μ κ³ λ €ν΄μΌ ν©λλ€.
Usage for VLLM
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer, pipeline
BASE_MODEL = "sh2orc/Llama-3-Korean-8B"
llm = LLM(model=BASE_MODEL)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'right'
instruction = 'μΉ΄λ ν λΆ κ²°μ μ λν΄μ μλ €μ€'
messages = [
{
"role": "system",
"content": "λΉμ μ νλ₯ν AI λΉμμ
λλ€. You are a great AI assistant."
},
{
"role": "user",
"content": instruction
},
]
prompt_message = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
outputs = llm.generate(prompt_message, SamplingParams(stop_token_ids=eos_token_id, temperature=0.6, top_p=0.8,max_tokens=4096))
for output in outputs:
propt = output.prompt
generated_text = output.outputs[0].text
print(generated_text)
Result
μΉ΄λ ν λΆ κ²°μ λ κ²°μ ν κΈμ‘μ μΌμ κΈ°κ° λμ λλ μ κ°λ λ°©μμΌλ‘, μΉ΄λμ¬μ μν΄ λμΆλ κΈμ‘μ κ°λ κ²μ λλ€. μΉ΄λ ν λΆ κ²°μ λ μΌμ ν κΈ°κ° λμ μνν μ μλ κΈμ‘μ μ ννμ¬ κ²°μ ν μ μμΌλ©°, μ΄ κ³Όμ μμ μ΄μλ₯Ό μ§λΆν΄μΌ ν©λλ€. μΉ΄λ ν λΆ κ²°μ λ μΌμλΆ κ²°μ λ³΄λ€ μ 리ν μ μμ§λ§, μ΄μλ₯Ό μ§λΆν΄μΌ νκΈ° λλ¬Έμ λΉμ©μ΄ μ¦κ°ν©λλ€.
- Downloads last month
- 1,853
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.