soroushmehr / openai_meta.txt

Created October 4, 2024 04:46 — forked from philschmid/openai_meta.txt

Leaked meta prompt, seen at https://x.com/amebagpt/status/1841177927569838519

	Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
	- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
	- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
	- Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
	- Conclusion, classifications, or results should ALWAYS appear last.
	- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements.
	- What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from p

soroushmehr / jupyter_gym_render.py

Created July 25, 2019 17:26 — forked from thomelane/jupyter_gym_render.py

OpenAI Gym render in Jupyter

	import gym
	from IPython import display
	import matplotlib
	import matplotlib.pyplot as plt
	%matplotlib inline

	env = gym.make('Breakout-v0')
	env.reset()
	img = plt.imshow(env.render(mode='rgb_array')) # only call this once
	for _ in range(100):

soroushmehr / pytorch-simple-rnn.py

Created August 10, 2018 21:05 — forked from spro/pytorch-simple-rnn.py

PyTorch RNN training example

	import torch
	import torch.nn as nn
	from torch.nn import functional as F
	from torch.autograd import Variable
	from torch import optim
	import numpy as np
	import math, random

	# Generating a noisy multi-sin wave

soroushmehr / subexpr.py

Created August 21, 2017 14:07 — forked from anj1/subexpr.py

	import types
	import tensorflow as tf
	import numpy as np

	# Expressions are represented as lists of lists,
	# in lisp style -- the symbol name is the head (first element)
	# of the list, and the arguments follow.

	# add an expression to an expression list, recursively if necessary.
	def add_expr_to_list(exprlist, expr):

soroushmehr / TestDense&HW.ipynb

Created June 1, 2017 20:10 — forked from mickypaganini/TestDense&HW.ipynb

Test Dense and Highway layers in Keras using the same inputs as in our lwtnn-test-highway.cxx

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soroushmehr / tmux_cheatsheet.markdown

Created May 16, 2017 13:34 — forked from henrik/tmux_cheatsheet.markdown

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

soroushmehr / latency.txt

Created January 31, 2017 16:21 — forked from jboner/latency.txt

Latency Numbers Every Programmer Should Know

	Latency Comparison Numbers
	--------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

soroushmehr / mnist_pytorch.py

Created January 23, 2017 17:57 — forked from kastnerkyle/mnist_pytorch.py

MNIST test in PyTorch, performance still TBD

	# Author: Kyle Kastner
	# License: BSD 3-Clause
	import torch as th
	import torch.nn as nn
	import torch.optim as optim
	from torch.autograd import Variable
	import torch.nn.functional as F
	from torch.utils.data import TensorDataset, DataLoader
	import time
	import numpy as np

soroushmehr / min-char-rnn.py

Created September 2, 2016 19:36 — forked from karpathy/min-char-rnn.py

Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

soroushmehr / gist:073bc400860636bc9b8f684cc1e9b050

Created September 2, 2016 19:31 — forked from karpathy/gist:587454dc0146a6ae21fc

An efficient, batched LSTM.

	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):

Soroush Mehri soroushmehr

tmux cheatsheet