Vinh Nguyen vinhnx

Rich Already Answered That!

A list of commonly asked questions, design decisions, reasons why Clojure is the way it is as they were answered directly by Rich (even when from many years ago, those answers are pretty much valid today!). Feel free to point friends and colleagues here next time they ask (again). Answers are pasted verbatim (I've made small adjustments for readibility, but never changed a sentence) from mailing lists, articles, chats.

How to use:

The link in the table of content jumps at the copy of the answer on this page.
The link on the answer itself points back at the original post.

Table of Content

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).

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

Pre-Transformer Models

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

General rules

Follow standard conventions.
Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
Boy scout rule. Leave the campground cleaner than you found it.
Always find root cause. Always look for the root cause of a problem.

	# 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

	# 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.

	class MyCell: UITableViewCell {
	let titleLabel = UILabel()
	let subtitleLabel = UILabel()

	override init(style: UITableViewCell.CellStyle, reuseIdentifier: String?) {
	super.init(style: style, reuseIdentifier: reuseIdentifier)
	}

	required init?(coder: NSCoder) { nil }

	import Foundation

	public protocol NetworkLoggable {
	func log<T: CustomStringConvertible>(
	label: String,
	value: T?,
	level: NetworkLogger,
	function: StaticString,
	line: UInt,
	file: String