Recent content on Reinforced Knowledge
The project stopped being a throughput project when I opened one of the fast outputs and found an answer. I was translating a large reasoning dataset into several European languages. The serving stack looked healthy. Requests completed. The inference server returned finish_reason = stop . Output files had the expected number of rows. The translated text had plausible length. Everything looked fine…
I came to verl for a research project: RL on function calling. I needed custom environments where the model interacts with tools and is rewarded on the call it makes. The capability to have custom rewards and eventually the room to modify or write the algorithms. Also, since I was familiar with them, I wanted FSDP and vLLM if I could get them. Oh, and not to forget, the capability to do long…
Introduction uv ’s documentation about build isolation is already great and has everything you need and I recommend and suggest to read before anything else. Read the whole documentation even, especially if you’re just starting with uv . I’m writing this article since these last days I had to either install flash-attn as a wheel directly or build it from source and since I struggled…
Hi everyone! This is going to be a short post, contrary to what I do usually, but I was going to reply to this Reddit post Disadvantages of Langchain/Langgraph in 2025 and found that my comment was too long and decided to make a Reddit post in and of itself so that maybe more people can see it and maybe it’ll resonate with others, and maybe we can gather more stories about using…
My first two articles ( part 1 and part 2 ) on Python project management and packaging gathered a lot of interest and I thought they were comprehensive enough for me not to come back and update them for a moment. But, about a month ago, PEP 751 – A file format to record Python dependencies for installation reproducibility , was accepted, 31 March 2025. At the time of publishing my first two…
In the first part we delved into the nitty gritty details of initializing and managing the dependencies of a Python package or project. This covered what is a Python package as opposed to a plain project (and other nomenclature), what is defined in the standard (e.g., project , build-system , tools and dependency-groups tables in pyproject.toml through the PEPs 518 , 621 and 735 ) and what are…
The goal of this guide is to provide a comprehensive guide to Python project management and packaging. We’ll explore concepts in the standard , like the different tables in pyproject.toml by revisiting the PEPs that led to what we have today. We’ll explain what was used before, why it needed to change, and how the changes provided by the PEPs solved the issues. This walkthrough of the…
The goal of this article is to provide a clear step-by-step guide starting from the fundamentals for deploying a Streamlit app on an AWS EC2 Instance and use your own domain name as well. So this guide will not only help you if you’re a beginner in AWS but also in deploying Streamlit apps. In this article I’ll deploy my inflacon project on my domaine inflacon.com . Though in this…
This post will only focus on some position encoding, namely, sinusoidal, T5 bias, ALiBi and Rotary. It’ll also discuss no position encoding (NoPE) and FIRE (functional interpolation of relative position encoding) This choice was made because the first methods are the most present in comparative benchmarks while for NoPE and FIRE, I found the ideas and the maths behind them interesting so I…
Paper: Generating Long Sequences with Sparse Transformers Before diving into this paper, I’ll have to say that this article might change in the future to include from scratch (but still relying on sparse backend operations) implementations of the theoretical elements introduced in this paper. In this article we’re not going to delve into the following elements introduced in the paper:…
(Part I) Modeling language can be done using a wide variety of techniques (e.g. Markov chains) and in this article, we’re going to focus neural networks approaches and specifically the decoder-only architectures, which are based on the transformer architecture introduced in the paper Attention Is All You Need , in 2017. If you’re interested in it, you can read my deep dive into the…
All the code in this article can be found on ReinforcedKnowledge/deep-learning-from-scratch/transformer Background The Transformer comes as an answer to the sequential computation constraint that recurrent neural networks (RNNs), long short-term memory neural networks (LSTMs) and gated recurrent neural networks (GRUs) suffer form. The Transformer leverages the attention mechanism that allows to…