Vinh Nguyen vinhnx

Falsehoods programmers believe about time

This is a compiled list of falsehoods programmers tend to believe about working with time.

Don't re-invent a date time library yourself. If you think you understand everything about time, you're probably doing it wrong.

Falsehoods

There are always 24 hours in a day.
February is always 28 days long.
Any 24-hour period will always begin and end in the same day (or week, or month).

Configuration Explanation for `DeepseekV3ForCausalLM`

Config: https://huggingface.co/deepseek-ai/DeepSeek-V3-Base/blob/main/config.json

This configuration file defines the architecture and hyperparameters for a model named DeepseekV3ForCausalLM, which is a causal language model (LM) based on the DeepseekV3 architecture. Below is an explanation of the key configurations:

Model Architecture

architectures: Specifies the model class, which is DeepseekV3ForCausalLM. This indicates the model is designed for causal language modeling (e.g., text generation).

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

	<!DOCTYPE html>
	<html lang="en">

	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1">
	<title>Vinh Nguyen \| iOS Engineer</title>

	<!-- SEO & sharing meta tags -->
	<meta name="description"

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	import re
	import torch
	from datasets import load_dataset, Dataset
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import LoraConfig
	from trl import GRPOConfig, GRPOTrainer

	# Ghostty Configuration File
	# Reference: https://ghostty.org/docs/config/reference

	# Theme and Appearance
	custom-shader-animation = false
	bold-is-bright = true
	macos-icon = "chalkboard"
	title = " "
	macos-titlebar-proxy-icon = hidden
	macos-window-shadow = false

	# install DSPy: pip install dspy
	import dspy

	# Ollam is now compatible with OpenAI APIs
	#
	# To get this to work you must include `model_type='chat'` in the `dspy.OpenAI` call.
	# If you do not include this you will get an error.
	#
	# I have also found that `stop='\n\n'` is required to get the model to stop generating text after the ansewr is complete.
	# At least with mistral.

	# vim:fileencoding=utf-8:foldmethod=marker

	# Include theme
	include ./theme.conf

	# ===== Config =====

	font_family Menlo
	font_size 15.0


	import UIKit

	extension UIViewController {
	func nxv_addChildViewController(_ childVC: UIViewController, containerView: UIView) {
	addChild(childVC)
	containerView.addSubview(childVC.view)
	containerView.translatesAutoresizingMaskIntoConstraints = false
	childVC.view.translatesAutoresizingMaskIntoConstraints = false