Kani: Building Language Model Applications Made Easy
At its core, Kani simplifies the interaction with language models, but it does so in a way that’s minimalist yet powerful. Let’s take a closer look at what makes Kani a standout framework:
Machine Learning
About Fine-Tuning vs. Prompt Tuning vs. Prompt Engineering
So, are you ready to differentiate for Fine-Tuning vs Prompt Tuning vs Prompt Engineering for your content needs? Dive in, learn about them.
What is Low-Rank Adaptation (LoRA) – How it works Example Code
LoRA aims to reduce the number of trainable parameters during fine-tuning. It involves freezing all original model parameters and introducing a pair of rank decomposition matrices alongside the weights.
What is Parameter Efficient Fine-Tuning (PEFT) – Coding Example
(PEFT) has emerged, enabling researchers and practitioners to maximize model performance with minimal data
What are Large Language Models (LLM)- Architecture Explained
The transformer architecture consists of two distinct parts, the encoder and the decoder, which work in conjunction with each other. These components share several similarities and play crucial roles in the model’s operation.