How to Use LLM CLI to Deploy the GPT-4o Model on DigitalOcean GPU Droplets

ssh root@<your-droplet-ip>

sudo apt install python3 python3-pip -y

sudo apt update && sudo apt upgrade -y

pip install llm

llm --version

Output
llm, version 0.16

llm --help

Output
Usage: llm [OPTIONS] COMMAND [ARGS]...

  Access large language models from the command-line

  Documentation: https://llm.datasette.io/

  To get started, obtain an OpenAI key and set it like this:

      $ llm keys set openai
      Enter key: ...

  Then execute a prompt like this:

      llm 'Five outrageous names for a pet pelican'

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  prompt*       Execute a prompt
  aliases       Manage model aliases
  chat          Hold an ongoing chat with a model.
  collections   View and manage collections of embeddings
  embed         Embed text and store or return the result
  embed-models  Manage available embedding models
  embed-multi   Store embeddings for multiple strings at once
  install       Install packages from PyPI into the same environment as LLM
  keys          Manage stored API keys for different models
  logs          Tools for exploring logged prompts and responses
  models        Manage available models
  openai        Commands for working directly with the OpenAI API
  plugins       List installed plugins
  replicate     Commands for working with models hosted on Replicate
  similar       Return top N similar IDs from a collection
  templates     Manage stored prompt templates
  uninstall     Uninstall Python packages from the LLM environment

llm keys set openai

Output
Enter key:<paste_your_api_key>

llm models

Output
OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
OpenAI Chat: gpt-3.5-turbo-16k (aliases: chatgpt-16k, 3.5-16k)
OpenAI Chat: gpt-4 (aliases: 4, gpt4)
OpenAI Chat: gpt-4-32k (aliases: 4-32k)
OpenAI Chat: gpt-4-1106-preview
OpenAI Chat: gpt-4-0125-preview
OpenAI Chat: gpt-4-turbo-2024-04-09
OpenAI Chat: gpt-4-turbo (aliases: gpt-4-turbo-preview, 4-turbo, 4t)
OpenAI Chat: gpt-4o (aliases: 4o)
OpenAI Chat: gpt-4o-mini (aliases: 4o-mini)
OpenAI Chat: o1-preview
OpenAI Chat: o1-mini
OpenAI Completion: gpt-3.5-turbo-instruct (aliases: 3.5-instruct, chatgpt-instruct)
Anthropic Messages: claude-3-opus-20240229 (aliases: claude-3-opus)
Anthropic Messages: claude-3-sonnet-20240229 (aliases: claude-3-sonnet)
Anthropic Messages: claude-3-haiku-20240307 (aliases: claude-3-haiku)
Anthropic Messages: claude-3-5-sonnet-20240620 (aliases: claude-3.5-sonnet)

llm -m gpt-4o "What is DigitalOcean GPU Droplet?"

Output
DigitalOcean GPU Droplets are specialized cloud-based virtual machines designed to handle high-performance computing tasks that require GPU acceleration. These Droplets are equipped with powerful graphics processing units (GPUs) that excel at parallel processing tasks, making them ideal for applications such as machine learning, data analysis, scientific computing, and rendering tasks that require significant computational power.

With GPU Droplets, users can leverage the processing capability of GPUs to speed up tasks that would otherwise take a long time on standard CPU-based instances. DigitalOcean provides these GPU instances with various configurations, allowing users to select the number of GPUs and the specific type of GPU that suits their workload requirements.

The key features and benefits of DigitalOcean GPU Droplets include:

1. **High Performance**: Access to powerful GPUs which are optimized for intensive computational tasks.

2. **Flexible Pricing**: Pay-as-you-go pricing model allows users to scale resources up or down based on demand.

3. **Scalability**: Easily scale GPU-powered compute resources according to the needs of your application.

4. **Integration and Ease of Use**: Easy integration with other DigitalOcean products and services, providing a unified cloud infrastructure handling both compute and storage needs.

5. **Developer-Friendly**: DigitalOcean's intuitive dashboard and developer-friendly infrastructure make it simpler for developers to deploy and manage GPU-intensive applications.

cat factorial.py | llm "Explain this code and find any bugs"

Output
The code provided is a Python implementation of a recursive function to calculate the factorial of a non-negative integer. Here's a breakdown of how the code works along with potential issues:

### Code Explanation:

1. **Function Definition**:
   - `def factorial(n):` defines a function named `factorial` that takes one argument `n`.

2. **Base Case**:
   - The first `if` statement `if n == 0 or n == 1:` checks whether `n` is either 0 or 1. The factorial of both 0 and 1 is 1, so if this condition is true, the function returns 1.

3. **Recursive Case**:
   - The `else:` clause is executed if `n` is greater than 1. The function calls itself recursively with the argument `n - 1` and multiplies the result by `n`. This recursive calling continues until it reaches the base case.

4. **Example Usage**:
   - The code asks the user to enter a number, converts that input into an integer using `int()`, and then prints the factorial of the given number by calling the `factorial` function.

### Bug and Issues:

1. **Input Validation**:
   - The code does not handle invalid inputs, such as negative numbers or non-integer values. Factorial is defined only for non-negative integers. If the user enters a negative integer or a non-integer (such as a string), the program will either return a stack overflow error (for negative numbers leading to infinite recursion) or crash with a `ValueError` for non-integer inputs.
   
   To fix this, you could add input validation to ensure `n` is a non-negative integer:
   while True:
       try:
           number = int(input("Enter a non-negative integer: "))
           if number < 0:
               print("Please enter a non-negative integer.")
           else:
               break
       except ValueError:
           print("Invalid input. Please enter a valid integer.")

llm "Give me more ways to optimize the code" -c

Output
Certainly! There are several ways to optimize the code for calculating the factorial. Here are some approaches, each with its own advantages:

### 1. Iterative Approach

The iterative approach is generally more efficient in terms of memory usage compared to the recursive approach:


def factorial(n):
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

### 2. Memoization

Using memoization can help store previously computed factorials to reduce redundant calculations. This is particularly useful if you're computing factorials of multiple numbers:


memo = {0: 1, 1: 1}  # Base case

def factorial(n):
    if n in memo:
        return memo[n]
    else:
        memo[n] = n * factorial(n - 1)
        return memo[n]


### 3. Using Python's Built-in Functions

Python has a built-in function for calculating factorials in the `math` module, which is highly optimized:

import math

number = int(input("Enter a non-negative integer: "))
print(f"The factorial of {number} is {math.factorial(number)}")