58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
import torch
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from modelscope import snapshot_download
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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from transformers import AutoConfig
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from modeling_qwen import QWenLMHeadModel
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from modeling_qwen import QwenRunner
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seed = 4321
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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model_dir = snapshot_download("qwen/Qwen-1_8B-Chat")
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# model_dir = "/home/colin/.cache/modelscope/hub/qwen/Qwen-1_8B-Chat"
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config, kwargs = AutoConfig.from_pretrained(
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"./",
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return_unused_kwargs=True,
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trust_remote_code=True,
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code_revision=None,
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_commit_hash=None,
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)
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model = QWenLMHeadModel(config)
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print(model)
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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model = model.from_pretrained(model_dir).cuda()
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model = model.eval()
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# model = model.train() # control by @torch.no_grad()
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# 可指定不同的生成长度、top_p等相关超参
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# model.generation_config = GenerationConfig.from_pretrained(
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# model_dir, trust_remote_code=True
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# )
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runner = QwenRunner(model)
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# 第一轮对话
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response, history, decode_tokens = runner.Chat(tokenizer, "东南亚国家日本的首都是什么市", "")
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print(decode_tokens)
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# <|im_start|>system
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# You are a helpful assistant.<|im_end|>
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# <|im_start|>user
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# 东南亚国家日本的首都是什么市<|im_end|>
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# <|im_start|>assistant
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# 日本的首都东京。<|im_end|><|endoftext|>
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# 第二轮对话
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response, history, decode_tokens = runner.Chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", "")
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print(decode_tokens)
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if decode_tokens.split("\n")[-2] != """这个故事告诉我们，只要我们有决心和毅力，就一定能够克服困难，实现我们的梦想。<|im_end|>""":
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raise ()
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