70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
# from modelscope.utils.constant import Tasks
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# from modelscope.pipelines import pipeline
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# prompt = "输入: 中国的首都在哪里\n输出: "
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# # task可选值为 抽取、分类。text为需要分析的文本。labels为类型列表，中文逗号分隔。
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# inputs = {'task': '抽取', 'text': '杭州欢迎你。', 'labels': '地名'}
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# # PROMPT_TEMPLATE保持不变
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# PROMPT_TEMPLATE = '输入: {text}\n{task}: {labels}\n输出: '
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# # prompt = PROMPT_TEMPLATE.format(**inputs)
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# pipeline_ins = pipeline(task=Tasks.text_generation, model='damo/nlp_seqgpt-560m', model_revision = 'v1.0.1', run_kwargs={'gen_token': '[GEN]'})
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# print(pipeline_ins(prompt))
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import torch
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import json
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.preprocessors import TextGenerationTransformersPreprocessor
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from modelscope.utils.config import Config
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from modelscope import snapshot_download
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from transformers import AutoConfig
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from modeling_bloom import BloomForCausalLM
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from tokenization_bloom_fast import BloomTokenizerFast
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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("damo/nlp_seqgpt-560m")
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config, kwargs = AutoConfig.from_pretrained(
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model_dir,
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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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tokenizer_config_file = "./tokenizer_config.json"
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if tokenizer_config_file is not None:
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with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
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init_kwargs = json.load(tokenizer_config_handle)
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init_kwargs.pop("tokenizer_class", None)
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init_kwargs.pop("tokenizer_file", None)
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saved_init_inputs = init_kwargs.pop("init_inputs", ())
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init_inputs = saved_init_inputs
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init_kwargs["vocab_file"] = None
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init_kwargs["added_tokens_file"] = None
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init_kwargs["special_tokens_map_file"] = "./special_tokens_map.json"
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init_kwargs["tokenizer_file"] = "./tokenizer.json"
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init_kwargs["name_or_path"] = model_dir
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tokenizer = BloomTokenizerFast(*init_inputs, **init_kwargs)
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model = BloomForCausalLM(config)
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model = model.from_pretrained(model_dir).cuda().train()
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prompt = "输入: 中国的首都在哪里\n输出: "
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prompt = "输入: 美国的首都在哪里\n输出: "
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input_ids = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=1024)
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input_ids = input_ids.input_ids.cuda()
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outputs = model.generate(input_ids, num_beams=4, do_sample=False, max_new_tokens=256)
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decoded_sentences = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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print(decoded_sentences[0])
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