hustvl / Vim
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
https://github.com/hustvl/Vim
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
https://github.com/hustvl/Vim
GitHub
GitHub - hustvl/Vim: [ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
[ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model - hustvl/Vim
5 Tips to Safely Use Credentials in Your Python Project
Learn 5 simple tips to help keep your Python credentials safe and fix problems quickly if they occur.
https://www.youtube.com/watch?v=OOvvQRBcrhI
Learn 5 simple tips to help keep your Python credentials safe and fix problems quickly if they occur.
https://www.youtube.com/watch?v=OOvvQRBcrhI
YouTube
5 Tips to Safely Use Credentials in Your Python Project
👷 Review code better and faster with my 3-Factor Framework: https://arjan.codes/diagnosis.
In this video, I'll share 5 simple tips to help keep your Python credentials safe and fix problems quickly if they occur.
🎓 ArjanCodes Courses: https://www.arjan…
In this video, I'll share 5 simple tips to help keep your Python credentials safe and fix problems quickly if they occur.
🎓 ArjanCodes Courses: https://www.arjan…
Alto
Serverless for data practitioners. The fastest way to run your code in the cloud. Effortlessly run scripts, functions, and Jupyter notebooks in virtual machines.
https://github.com/runprism/alto
Serverless for data practitioners. The fastest way to run your code in the cloud. Effortlessly run scripts, functions, and Jupyter notebooks in virtual machines.
https://github.com/runprism/alto
GitHub
GitHub - runprism/alto: Serverless for data practitioners. The fastest ⚡️ way to run your code in the cloud. Effortlessly run scripts…
Serverless for data practitioners. The fastest ⚡️ way to run your code in the cloud. Effortlessly run scripts, functions, and Jupyter notebooks in virtual machines. - runprism/alto
The simplest way to add a Django worker (Using AWS Chalice)
The article discusses the utilization of AWS Chalice to incorporate a Django worker, enabling the use of a lambda function as a serverless background worker for any application. This approach allows the lambda function to run in the background without blocking the main thread of the app, and it can call an endpoint on the Django app when completed, offering the advantage of using any Pyt...
https://www.photondesigner.com/articles/lambda-for-django
The article discusses the utilization of AWS Chalice to incorporate a Django worker, enabling the use of a lambda function as a serverless background worker for any application. This approach allows the lambda function to run in the background without blocking the main thread of the app, and it can call an endpoint on the Django app when completed, offering the advantage of using any Pyt...
https://www.photondesigner.com/articles/lambda-for-django
Photondesigner
The simplest way to add a Django worker (Using AWS Chalice) 🍷
Add an AWS lambda function, with async invocation for background tasks, to development and production (plus GitHub Actions deployment)
Let's build the GPT Tokenizer
The Tokenizer, essential for Large Language Models (LLMs), translates between strings and tokens, operating as a distinct stage with separate training sets and algorithms. This lecture builds the GPT series Tokenizer from scratch, uncovering peculiar behaviors in LLMs linked to tokenization. We explore these issues, attributing them to tokenization, and consider the ideal scenario of eli...
https://www.youtube.com/watch?v=zduSFxRajkE
The Tokenizer, essential for Large Language Models (LLMs), translates between strings and tokens, operating as a distinct stage with separate training sets and algorithms. This lecture builds the GPT series Tokenizer from scratch, uncovering peculiar behaviors in LLMs linked to tokenization. We explore these issues, attributing them to tokenization, and consider the ideal scenario of eli...
https://www.youtube.com/watch?v=zduSFxRajkE
YouTube
Let's build the GPT Tokenizer
The Tokenizer is a necessary and pervasive component of Large Language Models (LLMs), where it translates between strings and tokens (text chunks). Tokenizers are a completely separate stage of the LLM pipeline: they have their own training sets, training…
Using IPython Jupyter Magic Commands to Improve the Notebook Experience
A post on creating a custom IPython Jupyter Magic command.
https://towardsdatascience.com/using-ipython-jupyter-magic-commands-to-improve-the-notebook-experience-f2c870cab356
A post on creating a custom IPython Jupyter Magic command.
https://towardsdatascience.com/using-ipython-jupyter-magic-commands-to-improve-the-notebook-experience-f2c870cab356
Medium
Using IPython Jupyter Magic Commands to Improve the Notebook Experience
A post on creating a custom IPython Jupyter Magic command
explodinggradients / ragas
Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
https://github.com/explodinggradients/ragas
Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines
https://github.com/explodinggradients/ragas
GitHub
GitHub - explodinggradients/ragas: Supercharge Your LLM Application Evaluations 🚀
Supercharge Your LLM Application Evaluations 🚀. Contribute to explodinggradients/ragas development by creating an account on GitHub.
OS-Copilot
An self-improving embodied conversational agent seamlessly integrated into the operating system to automate our daily tasks.
https://github.com/OS-Copilot/FRIDAY
An self-improving embodied conversational agent seamlessly integrated into the operating system to automate our daily tasks.
https://github.com/OS-Copilot/FRIDAY
GitHub
GitHub - OS-Copilot/OS-Copilot: An self-improving embodied conversational agent seamlessly integrated into the operating system…
An self-improving embodied conversational agent seamlessly integrated into the operating system to automate our daily tasks. - GitHub - OS-Copilot/OS-Copilot: An self-improving embodied conversati...
Note Map: Effortlessly organize, visualize and chat about textual notes. Search, chat and brainstorm ideas with ease.
https://github.com/SudoACoder/note_map
https://github.com/SudoACoder/note_map
GitHub
GitHub - SudoACoder/note_map: Note Map: Effortlessly organize, visualize and chat about textual notes. Search, chat and brainstorm…
Note Map: Effortlessly organize, visualize and chat about textual notes. Search, chat and brainstorm ideas with ease. - GitHub - SudoACoder/note_map: Note Map: Effortlessly organize, visualize and...
So Google's Gemini Doesn't Like Python Programming and Sanskrit?
https://new.pythonforengineers.com/blog/so-evidently/
https://new.pythonforengineers.com/blog/so-evidently/
Python for Engineers
So Google's Gemini Doesn't Like Python Programming and Sanskrit?
I have been playing around with Googles Gemini Pro.
Recently, I wanted to write a blog on Python's decorators and wanted to get some ideas for practical projects I could build with them. Tried GPT4 first, it gave me the standard "log analyser" that all blogs…
Recently, I wanted to write a blog on Python's decorators and wanted to get some ideas for practical projects I could build with them. Tried GPT4 first, it gave me the standard "log analyser" that all blogs…
uv
An extremely fast Python package installer and resolver, written in Rust. Here is a
https://github.com/astral-sh/uv
An extremely fast Python package installer and resolver, written in Rust. Here is a
https://github.com/astral-sh/uv
GitHub
GitHub - astral-sh/uv: An extremely fast Python package and project manager, written in Rust.
An extremely fast Python package and project manager, written in Rust. - astral-sh/uv
minbpe
Minimal, clean, code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
https://github.com/karpathy/minbpe
Minimal, clean, code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
https://github.com/karpathy/minbpe
GitHub
GitHub - karpathy/minbpe: Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization. - karpathy/minbpe