Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys doeshe have now? A: He has 5 toys. He got 2 from mom, so after that he has 5 + 2 = 7 toys. Then he got 2 more from dad, soin total he has 7 + 2 = 9 toys. The answe...
Python# 用python代码,假设模型是"model",输入为"report_input"# 提示模板prompt_template = "阅读下列报告并生成一个简洁的摘要:\n{report_to_summarize}\n摘要:"# 插入待摘要的报告prompt = prompt_template.format(report_to_summarize=report_input)# 使用模型生成预测summary_prediction = model.generate(prompt...
微软亚洲研究院的研究员们提出了一种名为 LLM for Science 的范式。研究员们发现，大语言模型中知识的...
When not rigorously managed, LLMs may present security challenges by, for example, using sensitive or private information in a response. An AI technique called retrieval-augmented generation (RAG) can help with some of these issues by improving the accuracy and relevance of an LLM’s output. ...
When a prompt in a batch generation is too long for the model, llm.generate returns an unexpected number of outputs: In [11]: prompts = ["This is a short prompt", "This is a very long prompt " * 1000] ...: print(len(prompts)) 2 In [12]: ...
For years, the deep learning community has embraced openness and transparency, leading to massive open-source projects like HuggingFace. Many of the most profound ideas in deep learning (e.g…
7.持续学习：大模型领域的发展速度较快，保持持续学习的习惯，关注新的研究成果和技术趋势。大模型的概念...
Fine-tuning is well-suited for tasks with a clear and well-defined domain and access to large amounts of labeled data, such as sentiment analysis and database query generation for a specific product. It can update the base LLM's context with the new data and entirely remove any dependence...
from transformers import AutoModelForSeq2SeqLM from transformers import AutoTokenizer from transformers import GenerationConfig 准备从Hugging Face Hub提取对话数据 huggingface_dataset_name = "knkarthick/dialogsum" dataset = load_dataset(huggingface_dataset_name, revision="main") ...
ALBERT ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, 2019, Paper UniLM Unified Language Model Pre-training for Natural Language Understanding and Generation, 2019 Paper ELECTRA ELECTRA: PRE-TRAINING TEXT ENCODERS AS DISCRIMINATORS RATHER THAN GENERATORS, 2020, Paper T5 ...