mathdatech/Python
1
0
Fork 0
mirror of https://github.com/TheAlgorithms/Python.git synced 2025-03-20 05:29:48 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Pritam Das 2024-10-19 01:06:34 +05:30 committed by GitHub
parent d6609cbb73
commit 28b1f02fef
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 5 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
llm_experiments/README.md
View File

@ -1,9 +1,9 @@
# Mixtral-Experiment Series # LLM-Experiment Series
Welcome to the Mixtral-Experiment series! This series of notebooks and scripts aims to provide a comprehensive guide on investigating the internal workings of Large Language Models (LLMs), understanding how they process inputs, and experimenting with their architectures. Welcome to the LL-Experiment series! This series of notebooks and scripts aims to provide a comprehensive guide on investigating the internal workings of Large Language Models (LLMs), understanding how they process inputs, and experimenting with their architectures.
## Table of Contents ## Table of Contents
@ -20,11 +20,11 @@ Large Language Models (LLMs) have revolutionized the field of natural language p
## Series Overview ## Series Overview
The Mixtral-Experiment series will cover the following topics: The LLM-Experiment series will cover the following topics:
1. **Understanding LLM Architectures**: 1. **Understanding LLM Architectures**:
- An overview of popular LLM architectures like Transformers, BERT, and Mixtral. - An overview of popular open source LLM architectures like Whisper, Llama, and Mixtral.
- Detailed explanations of key components such as embedding layers, self-attention mechanisms, and Mixture of Experts (MoE) layers. - Key trouble shooting during experimentation
2. **Investigating Input Processing**: 2. **Investigating Input Processing**:
- How inputs are tokenized and embedded. - How inputs are tokenized and embedded.