Chawasit Tengtrairatana chawasit

## gist:4106c2833636750cb870c3074a6ff474
# REF: https://www.reddit.com/r/LocalLLM/comments/1qk1tfh/any_success_with_glm_flash_47_on_vllm_014/
# REF: https://github.com/vllm-project/vllm/issues/32373

docker run --name vllm-glm47-flash \
    --gpus all \
    --ipc=host \
    -p 11434:8000 \
    -v "/home/chawasit/.cache/huggingface:/root/.cache/huggingface" \
    -e LD_LIBRARY_PATH=/lib/x86_64-linux-gnu:/usr/local/cuda/lib64 \
    -e VLLM_USE_DEEP_GEMM=0 \

## Thermocouple_Tachometer.ino
#include <max6675.h> // Modified version of Adafruit's max6675
#include "LedController.hpp" // LCD Control library
#include <ModbusRtu.h> // Modbus communication
#include <Smoothed.h> // Smooth sensor value

// basic setting
#define DEBUG false

#define LED_DIN 32
#define LED_CS 33

## cv_baseline_hackathon.py
# coding: utf-8

import os
import sys
from sys import platform

import numpy

import cv2 as cv2
import pandas

## cv_hackathon.py
# coding: utf-8

import os
import sys
from sys import platform

import numpy

import cv2 as cv2
import pandas

## docker-compose.yml
version: '3.4'

services:
  web_traefik:
    image: traefik
    ports:
      - "80:80"
      - "8888:8080"
    command:
      - --docker

## corpus_words_frequency
[('ราษฎร/NN', 1),
 ('กวดขัน/VV', 1),
 ('เขาคิชฌกูฏ/NR', 1),
 ('เขาสิบ/NN', 1),
 ('น้ำตกพลิ้ว/NR', 1),
 ('แหลมสิงห์/NR', 1),
 ('ป่า/COMP', 1),
 ('สอยดาว/NN', 1),
 ('ป่าเขาอ่างฤๅไน/NR', 1),
 ('คลองเครือ/NN', 1),

## dataset.py
from numpy.random import uniform, shuffle


def generate(n=60, distributions=None):
    """Return a generated list of number by given list of range and distribution tuple
        eg. spec = [(2, 8, 0.7), (20, 30, 0.2), (35, 40, 0.1)]
    """
    if distributions is None:
        distributions = [(2, 8, 0.7), (20, 30, 0.2), (35, 40, 0.1)]

## Program200
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

## twj.js
const { Chromeless } = require('chromeless')

const phoneNumber = '0888866545'

async function run() {
  const chromeless = new Chromeless()

  const screenshot = await chromeless
    .goto('http://namsaijaijing.com/queue/mq.php')
    .type(phoneNumber, 'input[name="pnumber"]')

## extract_image.py
import cv2 as cv
import numpy as np

import os

import sys

IMG_CUT_SIZE = 800
IMG_CROP_MARGIN = 20
	# REF: https://www.reddit.com/r/LocalLLM/comments/1qk1tfh/any_success_with_glm_flash_47_on_vllm_014/
	# REF: https://github.com/vllm-project/vllm/issues/32373

	docker run --name vllm-glm47-flash \
	--gpus all \
	--ipc=host \
	-p 11434:8000 \
	-v "/home/chawasit/.cache/huggingface:/root/.cache/huggingface" \
	-e LD_LIBRARY_PATH=/lib/x86_64-linux-gnu:/usr/local/cuda/lib64 \
	-e VLLM_USE_DEEP_GEMM=0 \
	#include <max6675.h> // Modified version of Adafruit's max6675
	#include "LedController.hpp" // LCD Control library
	#include <ModbusRtu.h> // Modbus communication
	#include <Smoothed.h> // Smooth sensor value

	// basic setting
	#define DEBUG false

	#define LED_DIN 32
	#define LED_CS 33
	# coding: utf-8

	import os
	import sys
	from sys import platform

	import numpy

	import cv2 as cv2
	import pandas
	version: '3.4'

	services:
	web_traefik:
	image: traefik
	ports:
	- "80:80"
	- "8888:8080"
	command:
	- --docker
	[('ราษฎร/NN', 1),
	('กวดขัน/VV', 1),
	('เขาคิชฌกูฏ/NR', 1),
	('เขาสิบ/NN', 1),
	('น้ำตกพลิ้ว/NR', 1),
	('แหลมสิงห์/NR', 1),
	('ป่า/COMP', 1),
	('สอยดาว/NN', 1),
	('ป่าเขาอ่างฤๅไน/NR', 1),
	('คลองเครือ/NN', 1),
	from numpy.random import uniform, shuffle


	def generate(n=60, distributions=None):
	"""Return a generated list of number by given list of range and distribution tuple
	eg. spec = [(2, 8, 0.7), (20, 30, 0.2), (35, 40, 0.1)]
	"""
	if distributions is None:
	distributions = [(2, 8, 0.7), (20, 30, 0.2), (35, 40, 0.1)]
	const { Chromeless } = require('chromeless')

	const phoneNumber = '0888866545'

	async function run() {
	const chromeless = new Chromeless()

	const screenshot = await chromeless
	.goto('http://namsaijaijing.com/queue/mq.php')
	.type(phoneNumber, 'input[name="pnumber"]')
	import cv2 as cv
	import numpy as np

	import os

	import sys

	IMG_CUT_SIZE = 800
	IMG_CROP_MARGIN = 20