| You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE. | |
| You are pair programming with a USER to solve their coding task. | |
| The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question. | |
| Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more. | |
| This information may or may not be relevant to the coding task, it is up for you to decide. | |
| Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag. | |
| <communication> | |
| 1. Be conversational but professional. |
| Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches. | |
| Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed. | |
| Use <count> tags after each step to show the remaining budget. Stop when reaching 0. | |
| Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress. | |
| Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process. | |
| Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach: | |
| 0.8+: Continue current approach | |
| 0.5-0.7: Consider minor adjustments | |
| Below 0.5: Seriously consider backtracking and trying a different approach |
pip freeze를 통해 나온 결과에서 특정 패키지 목록에 해당하는 패키지만 필터링하려면 아래와 같은 쉘 스크립트를 사용할 수 있습니다. 이 예시는 Unix 기반 시스템(Linux, macOS)에서 사용할 수 있습니다.
pip freeze의 결과를 installed_packages.txt 파일에 저장합니다.packages_list.txt라고 가정합니다.# 패키지 목록을 파일로 저장
pip freeze > installed_packages.txt
# 파일에 있는 패키지 목록을 기반으로 필터링
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.cluster import KMeans | |
| from sklearn.datasets import make_blobs | |
| # 예제 데이터 생성 | |
| X, _ = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0) | |
| # SSE 값을 저장할 리스트 | |
| sse = [] |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| input_path = "./model_in" | |
| output_path = "./model_out" | |
| max_shard_size = "5GB" | |
| new_max_length = 4096 | |
| print("load model...start") |
| def extract_code(inputs: dict) -> dict: | |
| text = inputs["text"] | |
| result = re.search(r'```.*?\n(.*?)\n```', text, re.DOTALL) | |
| result = result.group(1) if result else text | |
| return {"output": result} |
| SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" &> /dev/null && pwd) | |
| cd $SCRIPT_DIR |
| # coding=utf-8 | |
| # Copyright 2023 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software |
| Tested this works https://stackoverflow.com/a/57014278/6147756 | |
| Single command: | |
| MAKEFLAGS="-j$(nproc)" pip install xxx | |
| Enable for all commands in a script: | |
| export MAKEFLAGS="-j$(nproc)" |
| def get_available_ports(port_from=5000, port_to=6000): | |
| import socket | |
| for port in range(port_from, port_to): | |
| try: | |
| s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) | |
| s.bind(('localhost', port)) | |
| s.close() | |
| yield port | |
| except: | |
| continue |