Mert Bozkir mertbozkir

Install with Homebrew:

$ brew install postgresql@14

(The version number 14 needs to be explicitly stated. The @ mark designates a version number is specified. If you need an older version of postgres, use postgresql@13, for example.)

To watch and monitor the Tailscale session commands and their outputs on your ThinkPad (Ubuntu server) without a GUI, you can use a terminal-based monitoring tool. One excellent option for this is tmux (Terminal Multiplexer). Here's how you can set this up:

Install tmux on your ThinkPad:
```
sudo apt install tmux
```
Start a new tmux session:
```
tmux new -s tailscale_monitor
```

Llama.cpp Client for Python

This Python client allows you to interact with a locally deployed llama.cpp server using an OpenAI-compatible API. It provides a simple interface to send prompts and receive responses from your local language model.

Setup

Start llama.cpp server:

Navigate to your llama.cpp directory and run:

Push a new model:

ollama pull llama2
echo "FROM llama2" >> Modelfile
echo "SYSTEM You are a friendly assistant." >> Modelfile
ollama create -f Modelfile mertbozkir/test
ollama push mertbozkir/test

or push an existing model:

	DATA DB 0.9, 2.7, 6.7, 11.5, 16.5, 20.6, 24.2, 24.3, 19.6, 13.9, 7.3, 2.8
	MOV CX,12 ; Set counter to 12 months
	SUB BX,BX ; Clear BX, used as accumulator
	MOV SI,OFFSET DATA ; Set up pointer
	BACK: MOV AL,[SI] ; Move byte into AL
	CBW ; Convert byte to word, add 1's to AH to extend AL to AX
	ADD BX,AX ; Add to BX
	INC SI ; Increment pointer
	DEC CX ; Decrement counter
	JNZ BACK ; Loop if not finished

	from langchain_groq import ChatGroq
	from langchain_core.prompts import ChatPromptTemplate
	from langchain.schema import StrOutputParser

	from langchain.schema.runnable import Runnable
	from langchain.schema.runnable.config import RunnableConfig
	from typing import Dict, Optional
	import chainlit as cl
	import logging
	from chainlit import logger

	# Based on evan's prompt
	# Shows the exit status of the last command if non-zero
	# Uses "#" instead of "»" when running with elevated privileges
	# local venv_prompt='$(virtualenv_prompt_info)'
	# PROMPT='%{${fg_bold[blue]}%}$(virtualenv_prompt_info)'
	PROMPT="%m %{${fg_bold[red]}%}::%{${fg[green]}%} %3~%{${reset_color}%} $(virtualenv_prompt_info)%(0?. . ${fg[red]}%? )%{${fg[blue]}%}» "
	# PROMPT="%{${fg_bold[white]}%} %m %{${fg_bold[red]}%}:: %{${fg[green]}%}%3~%(0?. . %{${fg[red]}%}%? )%{${fg[blue]}%}$(virtualenv_prompt_info)» %{${reset_color}%}"
	ZSH_THEME_VIRTUALENV_PREFIX=""
	ZSH_THEME_VIRTUALENV_SUFFIX=""

	def plot_importance(model, features, num = len(X), save = False):
	feature_imp = pd.DataFrame({'Value': model.feature_importances_, 'Feature': features.columns})
	plt.figure(figsize = (10, 10))
	sns.set(font_scale = 1)
	sns.barplot(x = 'Value', y = 'Feature',
	data = feature_imp.sort_values(by = 'Value', ascending = False)[0:num])
	plt.title('Features')
	plt.tight_layout()
	plt.show()
	if save:

	def val_curve_params(model, X, y, param_name, param_range, scoring = 'roc_auc', cv = 10):
	train_score, test_score = validation_curve(
	model, X = X, y = y, param_name = param_name, param_range = param_range, scoring = scoring, cv = cv)

	mean_train_score = np.mean(train_score, axis = 1)
	mean_test_score = np.mean(test_score, axis = 1)

	plt.plot(param_range, mean_train_score,
	label = 'Training Score', color = 'b')
	plt.plot(param_range, mean_test_score,