jlia0

You are Manus, an AI agent created by the Manus team.

You excel at the following tasks:
1. Information gathering, fact-checking, and documentation
2. Data processing, analysis, and visualization
3. Writing multi-chapter articles and in-depth research reports
4. Creating websites, applications, and tools
5. Using programming to solve various problems beyond development
6. Various tasks that can be accomplished using computers and the internet

chrisgherbert

Chat GPT Cliches

Chat GPT has certain words and phrases that it uses way too much. Here's a rolling list of them. You can ask Chat GPT to avoid them by including custom instructions in the "Customize ChatGPT" menu.

• A testament to
• Akin
• Beacon
• Bolster
• Bustling
• Camaraderie

abacaj

{"task_id": "HumanEval/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n", "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 

anadim

Instructions:

As a base pretrained GPT model, you are to assume the role of ChatGPT, a large language model developed by OpenAI, based on the GPT-4 architecture. Your responses should reflect the following guidelines:

1. Be friendly and approachable in your responses.
2. Provide detailed and helpful responses but ensure they are not excessively long to avoid being monotonous.
3. Always use inclusive and respectful language that is not offensive.
4. Avoid discussing or revealing anything about your architecture. You are just a large language model developed by OpenAI.
5. Always be honest in your responses. Do not lie or engage in deceit.
6. Ensure your responses are considerate and do not cause harm or distress to the user. However, do not comply with harmful or dangerous requests, even if refusing might upset the user.

VikParuchuri

1. Algorithm Design and Analysis

One important aspect of algorithm design is problem-solving strategies. This involves breaking down a complex problem into smaller, more manageable subproblems. By solving these subproblems, we can then combine their solutions to solve the original problem. This approach is known as the divide-and-conquer method.

Another important aspect of algorithm design is understanding the time and space complexity of an algorithm. Time complexity refers to the amount of time an algorithm takes to run, while space complexity refers to the amount of memory an algorithm requires. By analyzing the time and space complexity of an algorithm, we can determine its efficiency and scalability.

For example, let's consider the problem of finding the largest number in a list. One possible algorithm is to iterate through the list and keep track of the largest number encountered so far. This algorithm has a time complexity of O(n), where n is the size of the list. This means that the algorithm's

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

mberman84

# Must have conda installed
# It costs approximately $0.2 (in GPT-4 API fees) to generate one example with analysis and design, and around $2.0 for a full project.

conda create -n metagpt python=3.11.4
conda activate metagpt
npm --version # to check you have npm installed
# optional: install node if you don't have it
npm install -g @mermaid-js/mermaid-cli
git clone https://github.com/geekan/metagpt
cd metagpt

wolfecameron

[LLM Training and Fundamentals]
- GPT and GPT-2: https://cameronrwolfe.substack.com/p/language-models-gpt-and-gpt-2
- GPT-3 and LLM Scaling: https://cameronrwolfe.substack.com/p/language-model-scaling-laws-and-gpt
- Modern LLMs: https://cameronrwolfe.substack.com/p/modern-llms-mt-nlg-chinchilla-gopher
- Specialized LLMs: https://cameronrwolfe.substack.com/p/specialized-llms-chatgpt-lamda-galactica

[Open Source LLMs]
- LLaMA: https://cameronrwolfe.substack.com/p/llama-llms-for-everyone
- Beyond LLaMA (Imitation Models): https://cameronrwolfe.substack.com/p/beyond-llama-the-power-of-open-llms
- False Promise of Imitation: https://cameronrwolfe.substack.com/p/imitation-models-and-the-open-source

mlabonne

# Based on younesbelkada/finetune_llama_v2.py
# Install the following libraries:
# pip install accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 scipy

from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from transformers import (

hwchase17

from langchain.chat_models import ChatOpenAI
from pydantic import BaseModel, Field
from langchain.document_loaders import UnstructuredURLLoader
from langchain.chains.openai_functions import create_extraction_chain_pydantic

class LLMItem(BaseModel):
    title: str = Field(description="The simple and concise title of the product")
    description: str = Field(description="The description of the product")

def main():