While there is no prescribed way to evaluate LLM applications today, some guiding principles are emerging. Whether it’s choosing embedding models or evaluating LLM applications, focus on your specific task. This is especially applicable while choosing parameters for evaluation. Here are a few example...
It calculates the Key-Query-Value vectors of the single input token and append the Key-Values to the KV$ It processes only the single token through all layers of LM but calculate the causal attention of the single token with all the Key-Value vectors in KV$. ...
In very simple LLMs with only a few vectors and a few layers, researchers can understand how the LLM made decisions. However, it can take months for them to do this. Large LLMs use trillions of parameters and calculations per second which makes it impossible for humans to describe or unde...
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...
How we calculate sentiment scores at Sprout Sprout’s sentiment model uses deep neural networks (NNs), and in particular, large language models. LLMs work by considering the context of the entire block of text, reading the words from left to right and from right to left using the Bidirection...
Hi @MzeroMiko , did you able to figure out how to calculate FLOPs for selective scan? I used your script, and as you noted it is larger than what I expected? MzeroMiko commented Mar 1, 2024 @llmexperiment As addressed by @albertfgu , you can just return 9BLDN if you only use ...
anthropic_llm = ChatAnthropic() llm = openai_llm.with_fallbacks([anthropic_llm]) # Let's use just the OpenAI LLm first, to show that we run into an error with patch("openai.resources.chat.completions.Completions.create", side_effect=error): ...
The most popular LLMs are also some of the largest, meaning they can have more than 100 billion parameters. The intricate interconnections and weights of these parameters make it difficult to understand how the model arrives at a particular output.While the black box aspects of LLMs do not ...
“Show me current year to date vs. prior year to date sales figures for all sales orgs”. This can be accomplished in a number of ways, many of which have been explored in the following blog posts. Implementation of WTD, MTD, YTD in HANA using Input Parameters only Implementation of ...
Calculate each theme’s impact on satisfaction, loyalty, churn, and spend metrics. Here’s whatthat processlooks like: 💡 Thematic Expert Tip: Discover Escalating Issues.One of Thematic’s most unique features is the ability todiscover entirely new themes or significant shiftsin what customers ar...