AI Rank
中文
← Open Source
Infatoshi

fcc-intro-to-llms

Infatoshi/fcc-intro-to-llms on GitHub: Jupyter Notebook, Tutorials, ML/AI fundamentals.

TutorialsML/AI fundamentalsJupyter Notebook
Open on GitHub
Momentum
+0stars in 24 hours0.0%
847
Stars
346
Forks
+0
This week
1
Contributors
Created 2023-08-05 · Updated 2026-10-07 · #13245 today
Top developers
Infatoshi

Infatoshi/fcc-intro-to-llms is ranked 13,245 with 847 stars.

Same-category comparison

Infatoshi/fcc-intro-to-llms is ranked 13,245 among these 8 peers; huangjia2019/rag-in-action is ranked 13,250.

Same-category comparison
NameRankMetrics
openai/build-hours13,207
Language:
TypeScript
Stars:
852
Forks:
181
Description:
Build hours code to share.
DeepReinforcementLearning/DeepReinforcementLearningInAction13,212
Language:
Jupyter Notebook
Stars:
851
Forks:
350
Description:
Code from the Deep Reinforcement Learning in Action book from Manning, Inc.
MicrosoftDocs/ai-fundamentals13,222
Language:
Jupyter Notebook
Stars:
850
Forks:
550
Description:
Code samples for AI fundamentals.
haofanwang/ControlNet-for-Diffusers13,236
Language:
Python
Stars:
848
Forks:
52
Description:
Transfer the ControlNet with any basemodel in diffusers🔥.
huangjia2019/rag-in-action13,250
Language:
Jupyter Notebook
Stars:
847
Forks:
327
Description:
End-to-end RAG system design, evaluation, and optimization.
KeKe-Li/tutorial13,252
Stars:
846
Forks:
198
Description:
Deeplearning Algorithms Tutorial.
isseu/emotion-recognition-neural-networks13,256
Language:
Python
Stars:
846
Forks:
304
Description:
Emotion recognition using DNN with tensorflow.
ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post13,260
Stars:
845
Forks:
210
Description:
365 Days Computer Vision Learning Linkedin Post.
Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageJupyter Notebook
CategoryTutorials
SubcategoryML/AI fundamentals
Stars847
Forks346
Contributors1
Created date2023-08-05
Updated date2026-10-07
Stars in 1 day0
Stars in 7 days0
Rank13,245
Previous rank13,134
Rank change-111
7-day stars rank13,839
Previous 7-day stars rank13,853
7-day stars rank change14
Total stars rank12,733
Previous total stars rank12,711
Total stars rank change-22
README summary

FreeCodeCamp - Building LLMs from Scratch Google Colab for those who don't have a GPU: Dependencies (assuming windows): If you don't have an NVIDIA GPU, then the parameter will default to since . If device is defaulting to that is fine, you will just experience slower runtimes. All the links you should need are in this repo. I will add detailed explanations as questions and issues are posted. Visual Studio 2022 (for lzma compression algo) - OpenWebText Download if this doesn't work, default

to the wizard of oz mini dataset for training / validation Socials Twitter / X - My YouTube Channel - How to SSH from Mac to Windows - How to Setup Jupyter Notebooks in 5 minutes or less - Linkedin - Join My Discord Server - Schedule a 1-on-1: Research Papers: Attention is All You Need - A Survey of LLMs - QLoRA: Efficient Finetuning of Quantized LLMs -

Frequently asked questions

How many stars and forks are recorded?

Infatoshi/fcc-intro-to-llms has 847 stars and 346 forks.

What is the repository rank?

Infatoshi/fcc-intro-to-llms is ranked 13,245.

What language and category are recorded?

Infatoshi/fcc-intro-to-llms has 847 stars, uses Jupyter Notebook and belongs to Tutorials.

Which repository is closest in rank?

Infatoshi/fcc-intro-to-llms is compared with huangjia2019/rag-in-action, ranked 13,250 with 847 stars.

README

FreeCodeCamp - Building LLMs from Scratch

Google Colab for those who don't have a GPU: https://colab.research.google.com/drive/1_7TNpEEl8xjHlr9JzKbK5AuDKXwAkHqj?usp=sharing

Dependencies (assuming windows): pip install pylzma numpy ipykernel jupyter torch --index-url https://download.pytorch.org/whl/cu118

If you don't have an NVIDIA GPU, then the device parameter will default to 'cpu' since device = 'cuda' if torch.cuda.is_available() else 'cpu'. If device is defaulting to 'cpu' that is fine, you will just experience slower runtimes.

All the links you should need are in this repo. I will add detailed explanations as questions and issues are posted.

Visual Studio 2022 (for lzma compression algo) - https://visualstudio.microsoft.com/downloads/

OpenWebText Download

Socials

Twitter / X - https://twitter.com/elliotarledge

My YouTube Channel - https://www.youtube.com/channel/UCjlt_l6MIdxi4KoxuMjhYxg

How to SSH from Mac to Windows - https://www.youtube.com/watch?v=7hBeAb6WyIg&t=

How to Setup Jupyter Notebooks in 5 minutes or less - https://www.youtube.com/watch?v=eLmweqU5VBA&t=

Linkedin - https://www.linkedin.com/in/elliot-arledge-a392b7243/

Join My Discord Server - https://discord.gg/pV7ByF9VNm

Schedule a 1-on-1: https://calendly.com/elliot-ayxc/60min

Research Papers:

Attention is All You Need - https://arxiv.org/pdf/1706.03762.pdf

A Survey of LLMs - https://arxiv.org/pdf/2303.18223.pdf

QLoRA: Efficient Finetuning of Quantized LLMs - https://arxiv.org/pdf/2305.14314.pdf

Nearby in the ranking
Similar items