from transformers import pipeline messages = [ { "role": "user", "content": "Who are you?" }, ] pipe = pipeline("text-generation", model="qwen/Qwen2.5-3B") pipe(messages, do_sample=True, top_k=50, top_p=0.7) 执行
# pip install accelerate bitsandbytes import torch from transformers import pipeline pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True}) output = pipe("This is a cool example!", do_sample=True, top_p=0.95) 请注意,您可以将checkpoi...
transformers中的Pipeline类是 Hugging Face 工具箱中最通用的工具。LangChain 的设计主要是面向 RAG 和 Agent 应用场景,因此,在 Langchain 中流水线被简化为下面几个以文本为中心的任务:文本生成、文生文、摘要、翻译等。 用户可以使用from_model_id方法直接加载模型: fromlangchain_huggingfaceimportHuggingFacePipeline...
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) [INST] Explain what a Mixture of Experts is in...
Pipeliner CRM PIPware KPIs Pitney Bowes Data Validation [已弃用] Pitney Bowes Tax Calculator [已弃用] Pivotal Tracker Pixel Encounter (Independent Publisher) Pixela (Independent Publisher) PixelMe PKIsigning Placedog (Independent Publisher) Planful Planner Pling Plivo Plumsail Actions Plumsail Documents ...
# generator = pipeline("text-generation", model=model, tokenizer=tokenizer) generate_kwargs = { "max_new_tokens": 256, "do_sample": True, "temperature": 0.5, "min_p": 0.1, } generated_text = generator(messages, generate_kwargs=generate_kwargs) ...
问HuggingFace总结:指定`do_sample`和`num_beams`的效果EN来源：Deephub Imba本文约800字，建议阅读5...
"conversational": will return a ConversationalPipeline. "depth-estimation": will return a DepthEstimationPipeline. "document-question-answering": will return a DocumentQuestionAnsweringPipeline. "feature-extraction": will return a FeatureExtractionPipeline. "fill-mask": will return a FillMaskPipeline:. ...
from transformers import pipeline text_generator = pipeline("text-generation") print(text_generator("As far as I am concerned, I will", max_length=50, do_sample=False)) [{'generated_text': 'As far as I am concerned, I will be the first to admit that I am not a fan of the idea...
pipeline = pipeline("text-generation", model=model, model_kwargs={"torch_dtype": torch.bfloat16,"quantization_config": {"load_in_4bit": True} },)有关使用 Transformers 模型的更多详细信息，请查看模型卡。模型卡https://hf.co/gg-hf/gemma-2-9b 与 Google Cloud 和推理端点的集成 ...