This parameter controls the creative ability of your model. Here are a few of these parameters that can help you do so: Based on the application of the LLM, you can choose to increase or decrease the creative ability of the model. Given a prompt, it is possible to generate different outputs based on the parameters you set. Using stop words with a few-shot prompt Predictability vs. For example, if the model is prompted to complete the following sentence “Sky is blue, lemons are yellow and limes are” and you specify the stop word as just “.”, the model stops after finishing just this sentence, even if the token limit is higher than the generated sequence (Figure 2).įigure 3. This is another way to control the length of the output. Stop words are a set of character sequences that tells the model to stop generating any additional text, even if the output length has not reached the specified token limit. While there is a limit to the number of tokens ranging from 2048 to 4096 that NeMo models can accept for now, I don’t recommend hitting these limits as the model may generate off responses. You wouldn’t want the LLM to go on and on. The model does this in a loop appending the predicted token to the input sequence. There are parameters that can guide the model to decide when to stop generating any further text:Įarlier, I mentioned that the LLM is focused on generating the next token given the sequence of tokens. However, if you have something specific and want more granular control over the output, start experimenting with the other ones. In many cases, experimenting with the temperature parameter can get what you might need. Play around with these parameters and figure out the best combinations that work for your specific use case. Here are the key parameter categories to consider tweaking: To unlock the full potential of LLMs, explore the art of refining the outputs. In the next section, I discuss what those parameters are and how to tune them to get the best outputs. While the model decides what is the most probable output, you can influence those probabilities by turning some model parameter knobs up and down. General working flow of an LLM predicting the next word The model would then select the most likely word and add it to the prompt sequence.įigure 1. In the formula, is probability of given the context from previous tokens ( to and is the output of the neural network Here is the softmax equation for calculating the actual probability of a token: Those logits then are passed to a softmax function to generate probabilities for each possible output, giving you a probability distribution over the vocabulary. Logits are a function that represents probability values from 0 to 1, and negative infinity to infinity. Generationīehind the curtains, the model first generates logits for each possible output token. The prompt is broken down into a list of tokens that are taken as input by the LLM. NeMo uses byte-pair encoding to create these tokens. For example, the word “sandwich” would be broken down into the tokens “sand” and “wich”, whereas common words like “time” and “like” would be a single token. Tokens are words or chunks of characters. LLMs interpret the textual data as tokens. The prompt is broken down into smaller chunks called tokens and is sent as input to the LLM, which then generates the next possible tokens based on the prompt. Mechanism behind promptingīefore I get into the strategies to generate optimal outputs, step back and understand what happens when you prompt a model. For more information about getting started with LLMs, see An Introduction to Large Language Models: Prompt Engineering and P-Tuning. In this post, I discuss a few ways of getting around with LLMs, so that you can make the best out of them. NVIDIA NeMo offers pretrained language models that can be flexibly adapted to solve almost any language processing task while we can focus entirely on the art of getting the best outputs from the available LLMs. What does this mean for you? Interacting with the models today is the art of designing a prompt rather than engineering the model architecture or training data.ĭealing with LLMs can come at a cost given the expertise and resources required to build and train your models. However, the quality of this generated output is heavily dependent on the instruction that you give the model, which is referred to as a prompt. Having been trained on a vast corpus of text, LLMs can manipulate and generate text for a wide variety of applications without much instruction or training. It has transformed the way that we interact with technology. Large language models (LLMs) have generated excitement worldwide due to their ability to understand and process human language at a scale that is unprecedented.
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