t11s transmissions11

Running GitHub Copilot VSCode extension against local Code Llama model

Tested on NVIDIA RTX 4090, but these instructions also cover AMD and Mac in case you wanna try those.
This guide assumes you are running Linux (I ran this on Ubuntu).

Before you get excited:

Some remarks on Large Language Models

Yoav Goldberg, January 2023

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.

Intro

Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

	#include <stdio.h>
	#include <stdlib.h>
	#include <time.h>

	int xs[] = {7,62,2,46,73,43,26,82,5,95,57,56,44,21,40,79,13,6,9,8,72,59,65,81,60,78,13,85,87,58,48,25,32,47,67,4,31,19,33,1,92,14,53,89,84,54,29,10,17,3,77,70,45,97,34,23,86,55,15,64,68,83,76,41,18,39,94,22,74,11,69,49,12,35,20,90,100,98,36,63,91,38,66,93,50,96,61,71,75,37,52,88,30,28,99,27,42,51,80,24,16};

	int main()
	{
	int x = 0;
	for (int i = 1; i <= 100; ++i) {

	#!/usr/bin/env bash
	# Depends on terminal-notifier -> https://github.com/julienXX/terminal-notifier
	# Setup Details:
	# Save this as a bash file
	# [in claude code] Call /hooks
	# Select STOP
	# Paste fully qualified path of bash file
	json=$(cat)
	cwd=$(printf '%s' "$json" \| jq -r '.cwd // .tool_input.cwd // empty')
	[ -z "$cwd" ] && cwd="(no cwd field)"

	def verify_ddp_weights_equal(model: torch.nn.Module, atol: float = 1e-5) -> None:
	if hasattr(model, "module"):
	model = model.module

	world_size = get_world_size()
	for name, param in model.named_parameters():
	gathered_param = gather(param).reshape((world_size, -1))
	absolute_diffs = (gathered_param[None, 0, :] - gathered_param).abs()
	rank_params_eq = (absolute_diffs < atol).all()
	assert rank_params_eq, f"❌ param [{name}] not equal - got max_absolute_diff={absolute_diffs.max()}"

	// SPDX-License-Identifier: MIT
	pragma solidity ^0.8.22;

	import {wadLn, unsafeWadMul, unsafeWadDiv} from "solmate/utils/SignedWadMath.sol";

	/// @notice Approximated principal branch of [Lambert W function](https://en.wikipedia.org/wiki/Lambert_W_function)
	/// @dev Only supports the [1/e, 3+1/e] and [3+1/e, inf] interval
	/// @dev Approximate [1/e, 3+1/e] with a lookup table weighted average
	/// @dev Approximate and [3+1/e, inf] with ln(x) - ln(ln(x)) + ln(ln(x))/ln(x)
	contract Lambert {

	import traceback
	import openai
	import sys

	# list models
	models = openai.Model.list()

	def baka(error, character="tsundere",):
	exc_type, exc_value, exc_traceback = sys.exc_info()
	traceback_list = traceback.extract_tb(exc_traceback)

	coverage_report:
	name: Generate coverage report
	runs-on: ubuntu-latest
	steps:
	- uses: actions/checkout@v2

	- name: Install Foundry
	uses: onbjerg/foundry-toolchain@v1
	with:
	version: nightly

	// SPDX-License-Identifier: MIT
	pragma solidity ^0.8.13;

	library InsertionSort {
	function sort(uint256[] memory list) internal pure {
	// Algorithm:
	//
	// for i = 2 to n do
	// for j = 1 to i − 1 do // NOTE: we init do an extra sub instead of <=
	// if A[i] < A[j] then

	// UNTESTED!
	object "BribeContract" {
	code {
	let size := datasize("BribeRuntime")
	codecopy(0, dataoffset("BribeRuntime"), size)
	return(0, size)
	}
	object "BribeRuntime" {
	code {
	// Bribe by sending ETH. To try and claim, send a tx without ETH. Assumes that a tx with