Configuration parameters for LLMs | Clearly Explained
Want to optimize LLMs performance for your specific needs? In this video I will unravel the importance of adjusting configuration parameters, such as Random sampling, Greedy samply, as well as Top K and Top P sampling strategies to develop your own powerful LLM.
With Top K and Top P (Random sampling) you can easily control, how your LLM model should be creative or pragmatic.
Temperature and Max New Tokens are crucial parameters in developing LLMs because they directly influence the quality, coherence, and diversity of the generated text or next word/token generation.
All of these and more are clearly explained in this video, where every parameter is visualized and represented with examples.
By delving into these configuration parameters, you'll gain the knowledge and skills needed to fine-tune your LLM for various tasks, from generating creative stories to aiding in natural language processing tasks. Join us on this journey to enhance your understanding of LLMs and optimize their performance.
Remember that attention mechanism is the core of LLM archirecture. Because the softmax layer in the LLM calculates the probability distribution over the input tokens, indicating their relative importance for generating the next token.
The content of the video:
0:00 - Intro
1:10 - Attention mechanism for LLM
1:36 - Max New Tokens parameter
2:33 - Greedy vs. Random Sampling
4:56 - Top K parameter
5:59 - Top P parameter
6:51 - Summary of Top K vs. Top P
7:10 - Temperature parameter for LLMs
8:03 - The effect of low temperature for next token generation
9:05 - The effect of high temperature for next token generation
9:43 - The default temperature value in LLM
In case of any comments or suggestions, let me know in the comments below!
#LLM #largelanguagemodels #finetuning
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