CoffeeVampir3

from safetensors.torch import save_model, load_model
from safetensors import safe_open
import json

config_dict = {'version': '1.0'}
metadata = {k: json.dumps(v) if not isinstance(v, str) else v for k, v in config_dict.items()}

save_model(model, "model.safetensors", metadata=metadata)

# ...

CoffeeVampir3

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange

def tversky_multihead_similarity_vectorized_simple(x, features, prototypes, theta, alpha, beta, n_heads):
    batch_size, total_dim = x.shape
    d_head = total_dim // n_heads

    x = rearrange(x, 'b (h d) -> b h d', h=n_heads)

CoffeeVampir3

import express from "npm:express";
import { randomUUID } from "node:crypto";
import { McpServer } from "npm:@modelcontextprotocol/sdk/server/mcp.js";
import { StreamableHTTPServerTransport } from "npm:@modelcontextprotocol/sdk/server/streamableHttp.js";
import { z } from "npm:zod";

const PORT = 3000;
const MCP_ENDPOINT = "/mcp";

const app = express();

CoffeeVampir3

[TOOL_CALLS][{"name": "add", "arguments": {"a": 5, "b": 3}}][/TOOL_CALLS]

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;