n8programs N8python

Working on something different than what I was working on at the time I wrote this bio. All my work is CC0 unless otherwise specified.

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N8python / nano-mnist-replicate.py

Created October 3, 2024 05:11

Code to replicate score

	import torch
	from torchvision import datasets, transforms
	from tqdm import tqdm
	import numpy as np

	# Import the generated predict function
	from predict_function import predict

	# Load MNIST test dataset
	transform = transforms.Compose([transforms.ToTensor()])

N8python / math-assist-mlx.py

Created November 1, 2024 01:07

	import os
	import time
	from pynput import keyboard
	from datetime import datetime
	import subprocess
	import threading
	import tkinter as tk
	import queue

	# ML imports

N8python / cursed-convergence.c

Created November 1, 2024 07:15

It's cursed: https://www.desmos.com/calculator/asjxggbglo. What if you had only one discrete variable that you could plug into a sum sign, and *everything else had to be perfectly continuous* - no floor, no mod, no tricks. Could you even DO a generalized 2d riemann sum that converges as k -> infinity. Find out now (spoiler: you kinda can) - modi…

	#include <stdio.h>
	#include <math.h>
	#include <time.h>

	#define PI 3.14159265358979323846

	double s_inv(double x) {
	return asin(2.0 * (x - 0.5)) / PI + 0.5;
	}

N8python / scuffed-speculative.py

Created November 16, 2024 05:38


	def generate_speculative(
	model: nn.Module,
	draft_model: nn.Module,
	tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper],
	prompt: str,
	max_tokens: int = 100,
	verbose: bool = False,
	formatter: Optional[Callable] = None,
	**kwargs,

N8python / speculative.py

Created December 1, 2024 23:51

BIGGER

	from mlx_lm import load
	import mlx.core as mx
	from mlx.utils import tree_flatten, tree_map, tree_unflatten
	import numpy as np
	# Copyright © 2023-2024 Apple Inc.

	import contextlib
	import copy
	import glob
	import importlib

N8python / MORE.py

Last active December 7, 2024 22:01

faster every day

	from mlx_lm import load
	import mlx.core as mx
	from mlx.utils import tree_flatten, tree_map, tree_unflatten
	import numpy as np
	# Copyright © 2023-2024 Apple Inc.

	import contextlib
	import copy
	import glob
	import importlib

N8python / batch.py

Created December 31, 2024 00:39

Prototypes.


	def generate_batched(
	model: nn.Module,
	tokenizer: Union[PreTrainedTokenizer, TokenizerWrapper],
	prompt: str,
	batch_size: int,
	*,
	verbose: bool = False,
	formatter: Optional[Callable] = None,
	max_tokens: int = 256,

N8python / pretrain.py

Created January 15, 2025 23:41

Simple character-level pretraining in MLX. Gets a roughly billion tokens/day for an 18M parameter model on one M3 Max.

	import json
	import random
	import mlx.optimizers as optim
	import mlx.core as mx
	import mlx.nn as nn
	import numpy as np

	from tqdm import tqdm
	import time
	from datetime import datetime

N8python / orthomatrix.py

Created January 19, 2025 05:32

OPTIMIZE with me

	import torch
	import torch.nn as nn
	import torch.optim as optim

	# Define the differentiable orthonormal linear layer
	class OrthonormalLayer(nn.Module):
	def __init__(self, n):
	"""
	Initializes a learnable layer with an orthonormal weight matrix.
	:param n: Dimension of the square weight matrix.

N8python / matrix_exp.py

Created January 19, 2025 22:18

MLX matrix_exp.

	import mlx.core as mx
	@mx.compile
	def _compute_T1(A):
	"""I + A"""
	return mx.eye(A.shape[-1]) + A
	@mx.compile
	def _compute_T2(A):
	"""I + A + A^2/2"""
	A2 = A @ A
	return mx.eye(A.shape[-1]) + A + A2/2

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