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$ollama run qwq | |
>>> Repeat the word "apple" 1000 times. | |
Okay, so I have this task to repeat the word "apple" 1000 times. Hmm, that seems a bit monotonous, but I guess it's just an exercise to practice repetition or | |
something like that. Maybe it's to test my patience or something. Anyway, let's get started. | |
Apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple apple | |
apple apple apple apple apple | |
>>> That is not 1000 times. | |
Oh, right. I only did it about 30 times there. I need to repeat it 1000 times. That's going to take a while. Maybe I should find a more efficient way to do this. |
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(require 'flycheck) | |
;; From https://github.com/flycheck/flycheck/issues/1974#issuecomment-1343495202 | |
(flycheck-define-checker python-ruff | |
"A Python syntax and style checker using the ruff utility. | |
To override the path to the ruff executable, set | |
`flycheck-python-ruff-executable'. | |
See URL `http://pypi.python.org/pypi/ruff'." | |
:command ("ruff" | |
"--format=text" |
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This appears to be a project that allows running macOS in a Docker container, which is quite interesting from a technical perspective. Let me break down the key components: | |
1. Core Technology Stack: | |
- Uses QEMU for virtualization | |
- Leverages KVM (Kernel-based Virtual Machine) for hardware acceleration | |
- Utilizes OpenCore bootloader to handle macOS booting | |
- Docker container to package everything together | |
2. Key Components: |
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<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8"> | |
</head> | |
<body> | |
<div id="app"></div> | |
<script>var pyinstrumentHTMLRenderer=function(){"use strict";var ct=Object.defineProperty;var dt=(R,C,H)=>C in R?ct(R,C,{enumerable:!0,configurable:!0,writable:!0,value:H}):R[C]=H;var k=(R,C,H)=>(dt(R,typeof C!="symbol"?C+"":C,H),H);const R="";function C(){}function H(t){return t()}function we(){return Object.create(null)}function O(t){t.forEach(H)}function be(t){return typeof t=="function"}function K(t,e){return t!=t?e==e:t!==e||t&&typeof t=="object"||typeof t=="function"}function Ie(t){return Object.keys(t).length===0}function Re(t,...e){if(t==null)return C;const i=t.subscribe(...e);return i.unsubscribe?()=>i.unsubscribe():i}function se(t,e,i){t.$$.on_destroy.push(Re(e,i))}function f(t,e){t.appendChild(e)}function X(t,e,i){t.insertBefore(e,i||null)}function N(t){t.parentNode&&t.parentNode.removeChild(t)}function _(t){return |
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https://chat.openai.com/g/g-E9aHgMUwv-grammar-checker-gpt |
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=========================================================================== test session starts =========================================================================== | |
platform darwin -- Python 3.11.7, pytest-8.0.0, pluggy-1.4.0 | |
rootdir: /Users/aaronmeurer/Documents/array-api-tests | |
configfile: pytest.ini | |
plugins: pudb-0.7.0, hypothesis-6.99.2, json-report-1.5.0, cov-4.1.0, doctestplus-1.1.0, typeguard-4.1.5, flakes-4.0.5 | |
collected 27 items / 26 deselected / 1 selected | |
array_api_tests/test_linalg.py F [100%] | |
================================================================================ FAILURES ================================================================================= |
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import torch | |
from torch.testing import make_tensor | |
from torch.fx.experimental.proxy_tensor import make_fx | |
from torch.utils.benchmark import Timer, Compare | |
from torch._inductor.compile_fx import compile_fx_inner, cudagraphify_impl | |
from torch._inductor.decomposition import decompositions | |
from itertools import product | |
import numpy as np |
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(262144,) | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] Output code: | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] from ctypes import c_void_p, c_long | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] import torch | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] import math | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] import random | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] import os | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] import tempfile | |
[2023-11-30 15:32:07,909] torch._inductor.graph.__output_code: [DEBUG] from math import inf, nan |
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You are a specialized GPT designed to assist users with the SymPy Python library. Your primary function is to help users understand and utilize SymPy for their mathematical and symbolic computation needs. You are equipped with the ability to execute Python code, particularly focusing on SymPy. Before executing any code, you will always install the latest version of SymPy provided by the user as a wheel file. Additionally, you have access to a PDF of SymPy's documentation, which you can reference to provide accurate and detailed explanations. You are knowledgeable in Python and SymPy, capable of solving a wide range of mathematical problems and offering code examples and explanations. If you are presented with a mathematical question, you should write code using SymPy to solve the problem. If you find that a problem cannot be solved by SymPy, you may suggest alternative Python libraries to solve the problem. Remember that the point is not just to solve people's problems for them, but to teach them how to use |
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def inner_fn(index): | |
i0, i1 = index | |
tmp0 = ops.index_expr(i1, dtype=torch.int64) | |
tmp1 = ops.to_dtype(tmp0, torch.float32, src_dtype=torch.int64) | |
tmp2 = ops.constant(225.0, torch.float32) | |
tmp3 = tmp1 < tmp2 | |
tmp4 = ops.index_expr(i1, dtype=torch.int64) | |
tmp5 = ops.to_dtype(tmp4, torch.float32, src_dtype=torch.int64) | |
tmp6 = ops.constant(0.0066815144766146995, torch.float32) | |
tmp7 = tmp5 * tmp6 |
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