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Chinese-LLaMA-2-13B

Linly-Chinese-LLaMA2 ๅŸบไบŽ LLaMA2่ฟ›่กŒไธญๆ–‡ๅŒ–่ฎญ็ปƒ๏ผŒไฝฟ็”จ่ฏพ็จ‹ๅญฆไน ๆ–นๆณ•่ทจ่ฏญ่จ€่ฟ็งป๏ผŒ่ฏ่กจ้’ˆๅฏนไธญๆ–‡้‡ๆ–ฐ่ฎพ่ฎก๏ผŒๆ•ฐๆฎๅˆ†ๅธƒๆ›ดๅ‡่กก๏ผŒๆ”ถๆ•›ๆ›ด็จณๅฎšใ€‚

่ฎญ็ปƒ็ป†่Š‚ๅ’ŒbenchmarkๆŒ‡ๆ ‡่ฏฆ่ง ๐Ÿ’ป Github Repo

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Linly-AI/Chinese-LLaMA-2-13B-hf", device_map="cuda:0", torch_dtype=torch.float16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Linly-AI/Chinese-LLaMA-2-13B-hf", use_fast=False, trust_remote_code=True)
prompt = "ๅŒ—ไบฌๆœ‰ไป€ไนˆๅฅฝ็Žฉ็š„ๅœฐๆ–น๏ผŸ"

prompt = f"### Instruction:{prompt.strip()}  ### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
generate_ids = model.generate(inputs.input_ids, do_sample=True, max_new_tokens=2048, top_k=10, top_p=0.85, temperature=1, repetition_penalty=1.15, eos_token_id=2, bos_token_id=1, pad_token_id=0)
response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
response = response.lstrip(prompt)
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