ๅ ๅ ฅไธญๆ่ฏ่กจๅนถ็ปง็ปญ้ข่ฎญ็ปไธญๆEmbedding๏ผๅนถๅจๆญคๅบ็กไธ็ปง็ปญไฝฟ็จๆไปคๆฐๆฎ้finetuning๏ผๅพๅฐ็ไธญๆAlpaca-33Bๆจกๅใ
ๆจกๅ่ฝฌๆข็จๅฐ็็ธๅ ณbaseๅloraๆจกๅๅฆไธ๏ผ
- base-model: elinas/llama-30b-hf-transformers-4.29
- lora-model: ziqingyang/chinese-alpaca-lora-33b
่ฏฆๆ ๅฏๅ่๏ผhttps://github.com/ymcui/Chinese-LLaMA-Alpaca/releases/tag/v4.0
ไฝฟ็จๆนๆณๅ่
- ๅฎ่ฃ ๆจกๅๅ
pip install sentencepiece
pip install transformers>=4.28.0
- ็ๆๆๆฌ
import torch
import transformers
from transformers import LlamaTokenizer, LlamaForCausalLM
def generate_prompt(text):
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{text}
### Response:"""
tokenizer = LlamaTokenizer.from_pretrained('minlik/chinese-alpaca-33b-merged')
model = LlamaForCausalLM.from_pretrained('minlik/chinese-alpaca-33b-merged').half().to('cuda')
model.eval()
text = '็ฌฌไธไธช็ปไธๆ็็ไบบๆฏ่ฐ๏ผ'
prompt = generate_prompt(text)
input_ids = tokenizer.encode(prompt, return_tensors='pt').to('cuda')
with torch.no_grad():
output_ids = model.generate(
input_ids=input_ids,
max_new_tokens=128,
temperature=1,
top_k=40,
top_p=0.9,
repetition_penalty=1.15
).cuda()
output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output.replace(prompt, '').strip())
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 53.09 |
ARC (25-shot) | 59.3 |
HellaSwag (10-shot) | 78.43 |
MMLU (5-shot) | 57.69 |
TruthfulQA (0-shot) | 52.45 |
Winogrande (5-shot) | 76.09 |
GSM8K (5-shot) | 8.04 |
DROP (3-shot) | 39.67 |
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