GPT4All is open-source software for running large language models locally on a personal computer. It is based on GPT-3, does not require an internet connection for generation, and may be less effective than GPT-4-based managed models. section 9.2.3
Using GPT4All involves a practical local workflow:
- Install the application from gpt4all.io, following the instructions for the computer’s operating system. section 9.2.3
- Download a model before generating output; Mistral OpenOrca is one example used with the application. section 9.2.3
- Review saved models and chat history through the application’s settings and menus. section 9.2.3
- Treat generated answers as drafts: compare results with known formulas, expert knowledge, tests, or a stronger baseline. section 9.2 section 9.2.2
Generating a standard-deviation implementation
A prompt requesting pure Python code for the standard deviation of a list produces an algorithmic answer: calculate the mean, subtract it from each value, square the differences, average them, and take the square root. The result still requires checking, especially because population and sample standard deviations use different denominators. section 9.2.3 section 9.3
Listing 9.12 GPT-4All’s attempt to calculate standard deviation
To calculate the standard deviation of a list in Python, you can follow these steps:
1. Calculate the mean (average) of the numbers in the list.
2. Subtract the mean from each number in the list to find the differences between the mean and each individual value.
3. Square each difference obtained in step 2.
4. Find the average of these squared differences. This is the variance.
5. Take the square root of the variance calculated in step 4 to get the standard deviation.
Here's a Python code snippet that implements this algorithm:
def std_dev(numbers):
mean = sum(numbers) / len(numbers)
differences = [x - mean for x in numbers]
# Subtract the mean from each number.
squared_differences = [y * y for y in differences]
# Square each difference.
variance = sum(squared_differences) / len(numbers)
# Calculate average of squared differences (variance).
return math.sqrt(variance) # Return the standard deviation.
Inspect the representative GPT4All prompt and generated Python approach for a concrete example of local model-assisted coding. section 9.2.3
