Downloads · 30 days
11
2% of all-time downloads
sh2orc/llama-3-korean-8b-awq
llama-3-korean-8b-awq is a text generation model from sh2orc. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as llama3.
- Foundation Model Bllossom 8B - datasets - Koalpaca v1.1a - jojo0217/koreansafeconversation - AWQ Quantization - Paged Attention vLLM
Downloads · 30 days
11
2% of all-time downloads
All-time downloads
722
Public
Parameters
8.2B
6 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors6 GB · 100%
How the weights are stored.
I327B · 85%
From the Hugging Face model README
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer, pipeline
BASE_MODEL = "sh2orc/llama-3-korean-8b-awq"
llm = LLM(model=BASE_MODEL)
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'right'
instruction = '파이썬 merge sort 코드와 자세한 설명도 같이 제시해줘'
messages = [
{
"role": "system",
"content": "당신은 훌륭한 AI 비서입니다. 짧은 답변을 제시하고, 다음으로 상세 설명을 해주세요. You are a great AI assistant. Give a short answer, then elaborate. "
},
{
"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=0.8, top_p=0.95,max_tokens=2048))
for output in outputs:
propt = output.prompt
generated_text = output.outputs[0].text
print(generated_text)