yifanzz

[Project Name]

Every time you choose to apply a rule(s), explicitly state the rule(s) in the output. You can abbreviate the rule description to a single word or phrase.

Project Context

[Brief description ]

• [more description]
• [more description]
• [more description]

disler

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

ricklamers

Rank,Overall Acc,Model,Model Link,Organization,License,AST Summary,Exec Summary,Simple Function AST,Python Simple Function AST,Java Simple Function AST,JavaScript Simple Function AST,Multiple Functions AST,Parallel Functions AST,Parallel Multiple AST,Simple Function Exec,Python Simple Function Exec,REST Simple Function Exec,Multiple Functions Exec,Parallel Functions Exec,Parallel Multiple Exec,Relevance Detection,Cost ($ Per 1k Function Calls),Latency Mean (s),Latency Standard Deviation (s),Latency 95th Percentile (s)
1,78.76%,GPT-4-turbo-2024-04-09 (FC),https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo,OpenAI,Proprietary,81.70%,65.13%,73.82%,90.00%,33.00%,26.00%,89.50%,89.00%,74.50%,73.53%,83.00%,60.00%,70.00%,72.00%,45.00%,88.75%,4.79,5.68,6.67,20.07
2,73.71%,Claude-3-Opus-20240229 (FC tools-2024-04-04),https://www.anthropic.com/news/claude-3-family,Anthropic,Proprietary,70.35%,55.20%,80.91%,87.00%,61.00%,72.00%,91.00%,58.00%,51.50%,85.29%,85.00%,85.71%,74.00%,24.00%,37.50%,82.50%,30.65,12.63,3.

w-e-w

from datetime import datetime
import urllib.request
import base64
import json
import time
import os

webui_server_url = 'http://127.0.0.1:7860'

out_dir = 'api_out'

virattt

init27

# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

steven-tey

function ipToInt(ip: string) {
  return ip.split(".").reduce((acc, octet) => (acc << 8) + parseInt(octet), 0);
}

function isIpInCIDR(ip: string, cidr: string) {
  const [cidrIp, prefixLength] = cidr.split("/");
  const mask = -1 << (32 - parseInt(prefixLength));
  const ipInt = ipToInt(ip);
  const cidrIpInt = ipToInt(cidrIp);

hwchase17

from langchain.llms import Anthropic
from langchain.agents import load_tools, initialize_agent
from langchain.tools import AIPluginTool
PREFIX = """\n\nHuman: Answer the following questions as best you can. You have access to the following tools:"""
SUFFIX = """Begin!

Question: {input}
\n\nAssistant:
Thought:{agent_scratchpad}"""

lukestanley

# Credit to https://github.com/nsarrazin/serge - this is heavily copied from the API there and not very well yet but it might work.w
from typing import List, Optional
from uuid import UUID, uuid4
from pydantic import BaseModel, Field

from datetime import datetime

import subprocess, os
import asyncio

polluterofminds

pub resource SaleCollection: SalePublic {
    pub var forSale: @{UInt64: PinataPartyContract.NFT}

    pub var prices: {UInt64: UFix64}

    access(account) let ownerVault: Capability<&AnyResource{PinnieToken.Receiver}>

    init (vault: Capability<&AnyResource{PinnieToken.Receiver}>) {
        self.forSale <- {}
        self.ownerVault = vault