94 lines
3.1 KiB
Python
94 lines
3.1 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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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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# QWenLMHeadModel(
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# (transformer): QWenModel(
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# (wte): Embedding(151936, 2048)
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# (drop): Dropout(p=0.0, inplace=False)
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# (rotary_emb): RotaryEmbedding()
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# (h): ModuleList(
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# (0-23): 24 x QWenBlock(
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# (ln_1): RMSNorm()
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# (attn): QWenAttention(
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# (c_attn): Linear(in_features=2048, out_features=6144, bias=True)
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# (c_proj): Linear(in_features=2048, out_features=2048, bias=False)
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# (attn_dropout): Dropout(p=0.0, inplace=False)
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# )
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# (ln_2): RMSNorm()
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# (mlp): QWenMLP(
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# (w1): Linear(in_features=2048, out_features=5504, bias=False)
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# (w2): Linear(in_features=2048, out_features=5504, bias=False)
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# (c_proj): Linear(in_features=5504, out_features=2048, bias=False)
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# )
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# )
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# )
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# (ln_f): RMSNorm()
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# )
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# (lm_head): Linear(in_features=2048, out_features=151936, bias=False)
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# )
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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model = model.from_pretrained(model_dir, config=config, device_map="auto", trust_remote_code=True)
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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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# 第一轮对话
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response, history, decode_tokens = model.chat(tokenizer, "东南亚国家日本的首都是什么市", "", history=None)
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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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# # 第一轮对话
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# response, history, decode_tokens = model.chat(tokenizer, "你好", "", history=None)
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# print(decode_tokens)
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# # 你好！很高兴为你提供帮助。
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# 第二轮对话
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response, history, decode_tokens = model.chat(tokenizer, "给我讲一个年轻人奋斗创业最终取得成功的故事。", "", history=None)
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print(response)
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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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# 你好<|im_end|>
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# <|im_start|>assistant
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# 莎士比亚是头一个使用“你好”这个词的文学家，他在《哈姆雷特》中写道：“你是谁？你在哪儿？
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# ”他的这一段话，通常被认为是最早的使用“你好”这个词的文学记载。这句话在英国语中非常常见，
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# 特别是在正式或礼貌的情况下。<|im_end|><|endoftext|>
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