thesephist

import { formatPromptLines, polymer, Step } from "@thesephist/polymer";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

import { partition, PartitionBatch, unpartition } from "./partition";
import { ChatMessage, isAssistantMessage } from "./types";

export function mergeConsecutiveSpeakerMessages<T extends ChatMessage>(
  messages: T[]
): T[] {

thesephist

/**
 * Implementation of a naïve retrieval-based question answering LLM program.
 */

import {
  Document,
  formatPromptLines,
  getCompletionImpl,
  loggers,
  polymer,

thesephist

anysphere.pyright
apollographql.vscode-apollo
bradlc.vscode-tailwindcss
dbaeumer.vscode-eslint
denoland.vscode-deno
eamodio.gitlens
esbenp.prettier-vscode
github.vscode-pull-request-github
ms-azuretools.vscode-docker
ms-python.black-formatter

thesephist

import torch

from diffusers import FluxPipeline, AutoencoderKL
from diffusers.image_processor import VaeImageProcessor
from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel


ckpt_id = "black-forest-labs/FLUX.1-dev"
revision = "refs/pr/1"

thesephist

(()=>{let a=document.querySelectorAll('.bigmarker-cg-agenda-card-talk-name');for(const e of a)Object.assign(e.style,{overflow:'initial',display:'initial'})})()

thesephist

import { cascade } from "../src/cascade.ts";

export const summarize = cascade
  .chatCompletion("summarize", {
    model: "gpt-3.5-turbo",
    renderPrompt: (context: { title: string; content: string }) => [
      {
        type: "user",
        content: [
          `Write a one sentence summary about ${context.title}.\n\n${context.content}.`,

thesephist

; pull-all
;
; Run "git pull" en masse on all git repositories with a default remote under a
; specific directory.

(defn indent (s bit)
  (-> s
      (split '\n')
      (map (fn (line)
             (if (empty? line)

thesephist

import torch
import torch.nn as nn
import torch.nn.functional as F

from transformers import GPT2Tokenizer, GPT2LMHeadModel

ppl_model_name = 'gpt2-xl' if device == 'cuda' else 'gpt2'
ppl_tokenizer = GPT2Tokenizer.from_pretrained(ppl_model_name)
load_opts = {
    'device_map': 'auto',

thesephist

// Code to measure selection rects

// NOTE: this isn't actually JS... it's Oak. But I think the code is readable if you know JS :/

// NOTE: I *think* this event listener only works when attached to `document`.
with document.addEventListener('selectionchange') fn handleSelectionChange {
	if active := document.activeElement {
		? -> ?
		_ -> if active.classList.contains('textarea-itself') {
			false -> ?

thesephist

#!/usr/bin/env python

import os
import json
from tqdm import tqdm
from transformers import GPT2TokenizerFast

os.environ['TOKENIZERS_PARALLELISM'] = 'false'

FILENAME = './thesephist.jsonl'