stjordanis

{"task_id": "HumanEval/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n", "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 

stjordanis

This will help you to block, part of, the Huawei tracking and ADS using AdAway in VPN mode.

Simple add these links to the list:

ADS: https://gist.githubusercontent.com/cheadrian/22605ad0a96724d838fc76c7a866a837/raw/ads_huawei_blocklist.txt

Tracking: https://gist.githubusercontent.com/cheadrian/22605ad0a96724d838fc76c7a866a837/raw/metrics_tracking_huawei_blocklist.txt

stjordanis

# Must have conda installed
# It costs approximately $0.2 (in GPT-4 API fees) to generate one example with analysis and design, and around $2.0 for a full project.

conda create -n metagpt python=3.11.4
conda activate metagpt
npm --version # to check you have npm installed
# optional: install node if you don't have it
npm install -g @mermaid-js/mermaid-cli
git clone https://github.com/geekan/metagpt
cd metagpt

stjordanis

1. Algorithm Design and Analysis

One important aspect of algorithm design is problem-solving strategies. This involves breaking down a complex problem into smaller, more manageable subproblems. By solving these subproblems, we can then combine their solutions to solve the original problem. This approach is known as the divide-and-conquer method.

Another important aspect of algorithm design is understanding the time and space complexity of an algorithm. Time complexity refers to the amount of time an algorithm takes to run, while space complexity refers to the amount of memory an algorithm requires. By analyzing the time and space complexity of an algorithm, we can determine its efficiency and scalability.

For example, let's consider the problem of finding the largest number in a list. One possible algorithm is to iterate through the list and keep track of the largest number encountered so far. This algorithm has a time complexity of O(n), where n is the size of the list. This means that the algorithm's

stjordanis

# Title: StevenBlack/hosts
#
# This hosts file is a merged collection of hosts from reputable sources,
# with a dash of crowd sourcing via GitHub
#
# Date: 24 January 2021 21:02:05 (UTC)
# Number of unique domains: 59,895
#
# Fetch the latest version of this file: https://raw.githubusercontent.com/StevenBlack/hosts/master/hosts
# Project home page: https://github.com/StevenBlack/hosts

stjordanis

import lasagne
from lasagne.nonlinearities import rectify, softmax
from lasagne.layers import InputLayer, DenseLayer, DropoutLayer, batch_norm, BatchNormLayer
from lasagne.layers import ElemwiseSumLayer, NonlinearityLayer, GlobalPoolLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.init import HeNormal

def ResNet_FullPre_Wide(input_var=None, n=3, k=2):
    '''
    Adapted from https://github.com/Lasagne/Recipes/tree/master/papers/deep_residual_learning.

stjordanis

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 and experience preferred (super rare at this point).

My own notes from a few months back.

Background

• Survey of LLMS
• Self-attention and transformer networks

stjordanis

[LLM Training and Fundamentals]
- GPT and GPT-2: https://cameronrwolfe.substack.com/p/language-models-gpt-and-gpt-2
- GPT-3 and LLM Scaling: https://cameronrwolfe.substack.com/p/language-model-scaling-laws-and-gpt
- Modern LLMs: https://cameronrwolfe.substack.com/p/modern-llms-mt-nlg-chinchilla-gopher
- Specialized LLMs: https://cameronrwolfe.substack.com/p/specialized-llms-chatgpt-lamda-galactica

[Open Source LLMs]
- LLaMA: https://cameronrwolfe.substack.com/p/llama-llms-for-everyone
- Beyond LLaMA (Imitation Models): https://cameronrwolfe.substack.com/p/beyond-llama-the-power-of-open-llms
- False Promise of Imitation: https://cameronrwolfe.substack.com/p/imitation-models-and-the-open-source

stjordanis

# Based on younesbelkada/finetune_llama_v2.py
# Install the following libraries:
# pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 scipy

from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from transformers import (

stjordanis

# Based on younesbelkada/finetune_llama_v2.py
# Install the following libraries:
# pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 scipy

from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from transformers import (