smellslikeml

import torch
from fastapi import FastAPI, Request
from pydantic import BaseModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name = "name of model"
prompt_template = """ ### Input: {}  ### Response: {}"""
stop_token_ids = [0]

app = FastAPI()

smellslikeml

import asyncio
import nats
import uuid


async def aggregate_inferences(nats_url, request_subject, data, timeout=10):
    nc = await nats.connect(nats_url)

    responses = []

smellslikeml

# Launch nats-server
# wget https://huggingface.co/remyxai/stablelm-zephyr-3B_localmentor/resolve/main/ggml-model-q4_0.gguf -o stablelm-localmentor_2.gguf
import nats
import asyncio
from llama_cpp import Llama


async def llm_runner(nats_url, model_path, subject):
    nc = await nats.connect(nats_url)
    llm = Llama(model_path)

smellslikeml

#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#

smellslikeml

#!/bin/bash

# Ensure a list of clips is provided
if [ $# -eq 0 ]; then
    echo "Please provide a list of clips to process and concatenate."
    exit 1
fi

# Directory to store processed clips
mkdir -p processed_clips

smellslikeml

model:
  arch: video_llama
  model_type: pretrain_vicuna
  freeze_vit: True
  freeze_qformer: True
  max_txt_len: 160
  end_sym: "###"
  low_resource: True

  frozen_llama_proj: False

smellslikeml

import os
import time

import cv2
import hashlib
import numpy as np
from PIL import Image
from absl import logging
import tritonclient.http

smellslikeml

#!/usr/bin/env python3

import sys

import rospy
import rostopic

import numpy as np

import message_filters

smellslikeml

import cv2
import numpy as np
from collections import deque

# https://github.com/elliottzheng/face-detection
from face_detection import RetinaFace

# https://pyscenedetect.readthedocs.io/projects/Manual/en/latest/api/scene_manager.html#scenemanager-example
from scenedetect import VideoManager
from scenedetect import SceneManager

smellslikeml

# http://index-of.es/Varios-2/Practical%20Python%20AI%20Projects%20Mathemathical%20Models%20of%20Optimization%20Problems%20with%20Google%20OR-Tools.pdf
# coding: utf-8
"""
Example code for solving the transshipment problem. (pg. 111)

Input is an NxN numpy matrix (referenced here as D)
where row N is the demand and column N is the supply for each location 0 through N-1.

To solve, call the solve_model() function with matrix D as the input.
"""