cassc cassc

tokio vs threadpool

Tokio is an asynchronous runtime for the Rust programming language, and it is more than just thread pools. Here's a brief overview of how Tokio is implemented:

Event Loop: Tokio uses an event-driven architecture, leveraging an event loop to manage tasks and I/O events. The core of Tokio's runtime is the mio library, which provides a low-level event loop backed by OS-specific mechanisms like epoll (Linux), kqueue (BSD), and IOCP (Windows).
Futures and Tasks: Tokio's concurrency model is based on Rust's Future trait. Tasks are units of work that implement the Future trait, and they are polled by the runtime to make progress. When a task is not ready to make progress (e.g., waiting for I/O), it yields control back to the runtime.
Thread Pool:

Zero-shot examples refer to scenarios where a model is tasked with performing a specific task without having been explicitly trained on any data for that task. Instead, the model leverages its understanding from pre-training on other data to make predictions or inferences about the new task. This ability is particularly valuable for handling tasks or categories that were not seen during the model's training phase.

Zero-Shot Examples in Various Domains

1. Text Classification:

A model trained on general text data can classify text into categories it has never seen before by understanding the general concept of the categories.

Example:
- Input Text: "The weather today is sunny with a chance of showers in the evening."
New Categories: "Weather Report" vs. "Financial News"

Fine-tuning a pre-trained model like bigcode/starencoder on a large collection of Solidity source code without any labeling can be done through unsupervised learning, specifically using masked language modeling (MLM). Here’s a step-by-step guide to fine-tuning the model for your specific needs:

Steps to Fine-Tune the Model

1. Prepare the Dataset

Ensure you have a large collection of Solidity source code files. Combine these files into a single or multiple text files.

Example: Combining Solidity Files into a Text File

cat *.sol > all_solidity_code.txt

Updating cargo clippy is straightforward as it is part of the Rust toolchain. Here are the steps to ensure you have the latest version of cargo clippy:

Steps to Update Cargo Clippy

Update Rustup: Ensure that rustup, the Rust toolchain installer, is up to date. Open your terminal and run:
```
rustup self update
```

The issue you’re seeing is related to the limitations of the fdisk tool with the MBR (Master Boot Record) partition scheme, which can only handle partitions up to 2 TiB in size. However, your disk is 7.3 TiB, and to fully utilize it, you need to use the GPT (GUID Partition Table) scheme instead of MBR.

Here’s how to correctly create a GPT partition that supports your entire 7.3 TiB disk using gdisk, which is suitable for larger disks.

Steps to Partition and Format the Disk Using `gdisk`:

Install gdisk (if not already installed):
```
sudo apt-get update
```

sudo apt-get install gdisk

	{
	"00000014-mixed-4": {
	"env": {
	"currentCoinbase": "b94f5374fce5edbc8e2a8697c15331677e6ebf0b",
	"currentDifficulty": "0x200000",
	"currentRandom": "0x0000000000000000000000000000000000000000000000000000000000200000",
	"currentGasLimit": "0x26e1f476fe1e22",
	"currentNumber": "0x1",
	"currentTimestamp": "0x3e8",
	"previousHash": "0x044852b2a670ade5407e78fb2863c51de9fcb96542a07186fe3aeda6bb8a116d",

	from langchain_core.globals import set_debug
	from langchain_openai import ChatOpenAI
	from langchain_community.callbacks import get_openai_callback
	from langchain.chains import ConversationChain
	from langchain.chains import ConversationChain
	from langchain.memory import ConversationBufferMemory, ConversationSummaryBufferMemory


	set_debug(False)
	model_name = 'gpt-4o'

	from typing import List
	from pydantic import BaseModel, Field
	from langchain_core.globals import set_debug
	from langchain_core.output_parsers.json import JsonOutputParser
	from langchain_core.prompts.prompt import PromptTemplate
	from langchain_openai import ChatOpenAI
	from langchain_community.callbacks import get_openai_callback
	from langchain.chains import ConversationChain
	from langchain_community.llms import OpenAI

	from openai import OpenAI
	from sklearn.metrics.pairwise import cosine_similarity
	import numpy as np

	client = OpenAI()

	def cal_embedding(text, model="text-embedding-ada-002"):
	text = text.replace("\n", " ")
	embedding = client.embeddings.create(input = [text], model=model).data[0].embedding
	return np.reshape(embedding, (1, -1))

	import csv
	import argparse
	from web3 import Web3
	import os
	from datetime import datetime
	import time


	WEB3_PROVIDER_URI = 'https://eth-mainnet.g.alchemy.com/v2/token'