吴恩达《Transformer大语言模型工作原理|How Transformer LLMs Work》(deepseek-R1翻译中英字幕共计13条视频,包括:1.intro.zh_en、2.understanding language models(Word2Vec embeddings).zh_en、3.understanding language models( word embeddings).zh_en等,UP主更多精
In a new paper, researchers at the University of California, Berkeley, introduce Retrieval Augmented Fine Tuning (RAFT), a new technique that optimizes LLMs for RAG on domain-specific knowledge. RAFT uses simple but effective instructions and prompting techniques to fine-tune a language model in ...
Zero-shot Text-to-SQL:这种设置评估了预训练的LLM(大型语言模型)直接从表格中推断自然语言问题(NLQ)和SQL之间关系的能力,而无需任何示范示例。输入包括任务说明、测试问题以及相应的数据库。零样本文本到SQL用于直接评估LLM的文本到SQL能力。Single-domain Few-shot Text-to-SQL:这种设置适用于可以轻松构建示范示例的...
Desired output structure:Fine-tuning can teach models to follow a required structure or schema in outputs. For example, you can fine-tune a summarization model to consistently include key facts in a standardized template. Handling edge cases:Real-world data often contains irregularities and edge cas...
This is the main utilization metric we recommend for tracking for GPU Utilization. OpenAI charges based on the total number of tokens used by the prompt and response. (opens in new tab) Responsible AI As LLMs get used at large scale, it is critical to measure and detect any ...
early in the development of your LLM application. For each step of your pipeline, create a dataset of prompt and responses (considering the data sensitivity and privacy concerns of your application). When you’re ready to scale the application, you can use that dataset to fine-tune a model....
We want to fine-tune our LLM for several reasons, including adopting specific domain use cases, improving the accuracy, data privacy and security, controlling the model bias, and many others. With all these benefits, it’s essential to learn how to fine-tune our LLM to have one in producti...
Language models have become an essential part of the burgeoning field of artificial intelligence (AI) psychology. I discuss 14 methodological considerations that can be used to design more robust, generalizable studies that evaluate the cognitive abiliti
After choosing the model, the next step is to fine-tune it based on the custom knowledge you have. This will be possible using the embeddings you have generated. It will help the model learn and understand the specific context in which it will be used. ...
These parameters enable fine-tuning of LLM behavior, making them adaptable to diverse applications, from chatbots to content generation and translation. Shape the capabilities of LLMs LLMs have diverse applications, such as chatbots (e.g., ChatGPT), language translation, text generation, sentiment...