| name | japanese-tech-writing |
|---|---|
| description | 日本語の技術文書・書籍原稿の文章規範。段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、翻訳調の比喩と擬人化の禁止（「運ぶ」「効く」「開かれた問い」など）、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。 |
| license | Unlicense（https://gist.github.com/k16shikano/67625f2a7d96e3bbdfae8d571a936063） |
日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
以下の発表(2023/10/12)につき作成した、Pythonのパッケージ管理について学ぶ上で有益なリンクを集めたもの。
Pythonでの開発に関するベストプラクティス等を知ることは目的にしていない。
Package Managerを自作したり、開発にコミットするために必要なベースの知識を補うリンク集。
| import time | |
| import os | |
| import logging | |
| import random | |
| from datasets import load_dataset | |
| class QuantAutoGPTQ: | |
| def __init__(self, model_name_or_path, output_dir, dataset, | |
| num_samples=128, trust_remote_code=False, cache_examples=True, | |
| use_fast=True, use_triton=False, bits=[4], group_size=[128], damp=[0.01], |
Yoav Goldberg, April 2023.
With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much
| import { Client, ServiceError, Metadata, CallOptions, ClientUnaryCall } from '@grpc/grpc-js'; | |
| import { Message } from 'google-protobuf'; | |
| type OriginalCall<T, U> = (request: T, metadata: Metadata, options: Partial<CallOptions>, callback: (error: ServiceError, res: U) => void) => ClientUnaryCall; | |
| type PromisifiedCall<T, U> = ((request: T, metadata?: Metadata, options?: Partial<CallOptions>) => Promise<U>); | |
| export type Promisified<C> = { $: C; } & { | |
| [prop in Exclude<keyof C, keyof Client>]: (C[prop] extends OriginalCall<infer T, infer U> ? PromisifiedCall<T, U> : never); | |
| } |
| [ | |
| { | |
| "name": "Pok3r macOS (Karabiner fn)", | |
| "author": "tuxedocat", | |
| "switchMount": "cherry", | |
| "switchBrand": "cherry", | |
| "switchType": "MX3A-L1xx" | |
| }, | |
| [ | |
| { |
| import numpy as np | |
| import pandas as pd | |
| #import lightgbm as lgb | |
| from optuna.integration import lightgbm as lgb | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.utils import check_X_y, safe_sqr | |
| from sklearn.feature_selection.base import SelectorMixin | |
| from lightgbm import Booster |
