However, as the adoption of generative AI accelerates, companies will need to fine-tune their Large Language Models (LLM) using their own data sets to maximize the value of the technology and address their unique needs. There is an opportunity for organizations to leverage their Content Knowledge...
First, the volume of the training data is critical. As large language models (LLMs), Meta's LLaMA has 65 billion parameters and 4.5 TB of training data, while OpenAI's GPT-3.5 has 175 billion parameters and 570 GB of training data. Although LLaMA has less than half the parameters of G...
Now I'm using LoRA to tune a LLM (ChatGLM-6B) using 2 * A800 80G. I've got some findings that really confuse me. The first problem: Setting device_map="auto” to my understanding means setting model parallelization (MP), which will put the model layers into different devices. Thus,...
Reduce dataset size or use a GPU with more memory: If your dataset is too large, you might need to reduce its size or use a GPU with more memory. Please note that the code provided does not directly interact with CUDA or GPU, it's the underlying Faiss library that does. Therefore, ...
To reduce hallucinations about unknown categories, we can make a small change to our prompt. Let’s add the following text: If a user asks about another category respond: "I can only generate quizzes for Art, Science, or Geography" Here’s the complete updated prompt: Write a quiz for...
Also:8 ways to reduce ChatGPT hallucinations OpenAI recommends you provide feedback on what ChatGPT generates by using the thumbs-up and thumbs-down buttons to improve its underlying model. You can also join the startup'sBug Bounty program, which offers up to $20,000 for reporting se...
When we provide prompts or queries to an LLM, as well as any relevant contextually aware input content, it processes the entire input, which can be computationally expensive, especially for longer prompts with lots of data. Prompt compression aims to reduce the size of the input by condensing ...
and Microsoft Security Response Center (MSRC)—we enable our customers to reduce the alert signal to noise ratio by providing them with prioritized incidents that render end-to-end attacks in complete context rather than giving them an endless list of uncorrelated alerts. This will lead...
This post walked through the process of customizing LLMs for specific use cases using NeMo and techniques such as prompt learning. From a single public checkpoint, these models can be adapted to numerous NLP applications through a parameter-efficient, compute-efficient process. ...
For example, when a user submits a prompt to GPT-3,it must access all 175 billion of its parametersto deliver an answer. One method for creating smaller LLMs, known assparse expert models, is expected to reduce the training and computational costs for LLMs, “resulting in ...