azu

export const requireComment = {
  meta: {
    type: "suggestion",
    docs: {
      description: "useEffectにはコメントでの説明が必須です。",
    },
    schema: [],
    messages: {
      requireCommentOnUseEffect: `useEffectにはコメントでの説明が必須です。

awni

"""
A minimal, fast example generating text with Llama 3.1 in MLX.

To run, install the requirements:

    pip install -U mlx transformers fire

Then generate text with:

    python l3min.py "How tall is K2?"

veekaybee

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 of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

jvelezmagic

"""QA Chatbot streaming using FastAPI, LangChain Expression Language , OpenAI, and Chroma.

Features
--------
- Persistent Chat Memory:
    Stores chat history in a local file.
- Persistent Vector Store:
    Stores document embeddings in a local vector store.
- Standalone Question Generation:
    Rephrases follow-up questions to standalone questions in their original language.

doraTeX

#!/bin/bash
SCRIPTNAME=$(basename "$0")

function realpath () {
  f=$@;
  if [ -d "$f" ]; then
    base="";
    dir="$f";
  else
    base="/$(basename "$f")";

rain-1

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

• Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

aspick

requirements

• wasi-vfs
• wasi-preset-args
• wasmer

steps

# follow `ruby.wasm` tutorial
curl -LO https://github.com/ruby/ruby.wasm/releases/latest/download/ruby-head-wasm32-unknown-wasi-full.tar.gz

yusukebe

import { Client } from '../../../src/middleware/client'
import type { AppType } from './server'

const client = new Client<AppType>('http://127.0.0.1:8787/api')

const res = await client.json('/posts', {
  id: 123,
  title: 'hello',
})

dgcoffman

const isUseEffect = (node) => node.callee.name === 'useEffect';

const argumentIsArrowFunction = (node) => node.arguments[0].type === 'ArrowFunctionExpression';

const effectBodyIsSingleFunction = (node) => {
  const { body } = node.arguments[0];

  // It's a single unwrapped function call:
  //   `useEffect(() => theNameOfAFunction(), []);`
  if (body.type === 'CallExpression') {

mislav

// ~/docker/config.json
{
	"credsStore": "desktop",
	"credHelpers": {
		"docker.pkg.github.com": "gh",
		"ghcr.io": "gh"
	}
}