CoffeeVampir3

import { StreamingWebSocket, CancellationError } from "./streamingWebSocket.ts";

/**
 * Example of client-side streaming inference implementation
 * Following the exact flow described in the specification
 */
async function streamInference(
  socket: StreamingWebSocket,
  onContent: (content: string) => void
): Promise<void> {

CoffeeVampir3

import {
  InferenceJobArguments,
  InferenceProcessor,
} from "../frontend/InferenceProcessor.ts";
import { ClientStream } from "../router/clientStream.ts";
import { delay } from "https://deno.land/[email protected]/async/delay.ts";
import { StreamingState } from "./types.ts";

interface StreamingJob {
  id: string;

CoffeeVampir3

#ifndef RULE_STREAM_HPP
#define RULE_STREAM_HPP

#include <multisampler.hpp>
#include <slot.hpp>

#include "llama.h"
#include "sampling.h"
#include <unordered_map>
#include <utility>

CoffeeVampir3

export module Logging;
import std;

/*
Works exactly like std::print. Fail and ass will show verbose logging data (failure location.)
Logging::info("hello world");
Logging::warn("{} + {}", "sweg and", 5);
Logging::fail("{} {}", "the ", " game");
Logging::ass(false == true, "False was not true"); //outputs to cerr
*/

CoffeeVampir3

(await (async (startPage = 0, autoClearConsole = true) => {

  const getCookie = (name) => {
    const value = `; ${document.cookie}`;
    const parts = value.split(`; ${name}=`);
    if (parts.length === 2) return parts.pop().split(';').shift();
  }

  const callCacheApi = async (params = {}) => {
    const defaultParams = {

CoffeeVampir3

export module Vulkan;
import <stdexcept>;
import Validation;

#define GLFW_INCLUDE_VULKAN
#include <GLFW/glfw3.h>

export namespace Vulkan {
    auto CreateInstance() {
        VkInstance instance;

CoffeeVampir3

export module VulkanInstance;

#define GLFW_INCLUDE_VULKAN
#include <GLFW/glfw3.h>

import VulkanValidation;

export namespace Vulkan {
    auto CreateInstance() {
        VkInstance instance;

CoffeeVampir3

from exllamav2 import(
    ExLlamaV2Tokenizer
)

def encode_message(tokenizer: ExLlamaV2Tokenizer, role: str, message: str) -> list:
    tokens = []
    start_token = tokenizer.single_id("<start_of_turn>")
    end_token = tokenizer.single_id("<end_of_turn>")
    
    tokens.append(start_token)

CoffeeVampir3

use candle_transformers::models::quantized_llama as model;
use candle_transformers::generation::{LogitsProcessor, Sampling};
use candle_core::quantized::{gguf_file};
use candle_core::Tensor;
pub use candle_core::Device;
pub use tokenizers::Tokenizer;

pub struct StreamableModel {
    model: model::ModelWeights,
    tokenizer: tokenizers::Tokenizer,

CoffeeVampir3