💫 Community Model> CodeGemma 7b Instruct by Google
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Model creator: Google
Original model: google/codegemma-7b-it
GGUF quantization: provided by bartowski based on llama.cpp release b2589
Model Summary:
CodeGemma 7b Instruct is the first in a series of coding models released by Google. As an instruct model, it specializes in being asked coding related questions, but can also function as an autocomplete/fill-in-middle model for tools like co-pilot.
This model is perfect for general coding questions or code generation.
Prompt Template:
Choose the 'Google Gemma Instruct' preset in your LM Studio.
Under the hood, the model will see a prompt that's formatted like so:
<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
Note that this model does not support a System prompt.
Use case and examples
This model should be used for coding purposes.
Coding 1
<start_of_turn>user
Write a quicksort in C++<end_of_turn>
<start_of_turn>model
#include <iostream>
using namespace std;
int partition(int arr[], int low, int high) {
int pivot = arr[high];
int i = (low - 1);
for (int j = low; j <= high - 1; j++) {
if (arr[j] <= pivot) {
i++;
swap(arr[i], arr[j]);
}
}
swap(arr[i + 1], arr[high]);
return (i + 1);
}
void quickSort(int arr[], int low, int high) {
if (low < high) {
int pi = partition(arr, low, high);
quickSort(arr, low, pi - 1);
quickSort(arr, pi + 1, high);
}
}
int main() {
int arr[] = {10, 7, 8, 9, 1, 5};
int n = sizeof(arr) / sizeof(arr[0]);
quickSort(arr, 0, n - 1);
cout << "Sorted array: ";
for (int i = 0; i < n; i++) {
cout << arr[i] << " ";
}
return 0;
}
<end_of_turn>
Coding 2
<start_of_turn>user
Write a merge sort in java<end_of_turn>
<start_of_turn>model
public class MergeSort {
public static void mergeSort(int[] arr) {
int n = arr.length;
if (n < 2) {
return;
}
int mid = n / 2;
int[] left = new int[mid];
int[] right = new int[n - mid];
for (int i = 0; i < mid; i++) {
left[i] = arr[i];
}
for (int i = mid; i < n; i++) {
right[i - mid] = arr[i];
}
mergeSort(left);
mergeSort(right);
merge(arr, left, right);
}
public static void merge(int[] arr, int[] left, int[] right) {
int i = 0;
int j = 0;
int k = 0;
while (i < left.length && j < right.length) {
if (left[i] <= right[j]) {
arr[k] = left[i];
i++;
} else {
arr[k] = right[j];
j++;
}
k++;
}
while (i < left.length) {
arr[k] = left[i];
i++;
k++;
}
while (j < right.length) {
arr[k] = right[j];
j++;
k++;
}
}
public static void main(String[] args) {
int[] arr = {5, 2, 4, 6, 1, 3};
mergeSort(arr);
for (int i = 0; i < arr.length; i++) {
System.out.print(arr[i] + " ");
}
}
}
<end_of_turn>
Coding 3
<bos><start_of_turn>user
How do you write a for loop in react?<end_of_turn>
<start_of_turn>model
The syntax for a for loop in React is:
for (let i = 0; i < array.length; i++) {
// Code to be executed for each iteration
}
Where:
* `i` is the loop variable, which starts at 0 and increments by 1 with each iteration.
* `array` is the array you want to iterate over.
* `array.length` is the length of the array.
* `// Code to be executed for each iteration` is the code you want to execute for each iteration of the loop.
Here is an example of how to use a for loop to render a list of items:
const items = ["item 1", "item 2", "item 3"];
const listItems = items.map((item, index) => (
<li key={index}>{item}</li>
));
return (
<ul>
{listItems}
</ul>
);
In this example, the `map()` method is used to create a new array of `li` elements. The `key` prop is used to uniquely identify each `li` element.
Here are some other things to keep in mind when using for loops in React:
* You can use any variable name for the loop variable.
* You can use any condition for the loop condition.
* You can use any code inside the loop.
* You can use nested for loops.
Here are some resources that you may find helpful:
* **For Loop in JavaScript:** https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Statements/for
* **Map() method:** https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/map
* **Key prop:** https://reactjs.org/docs/lists-and-keys.html#keys<end_of_turn>
Coding related question
<bos><start_of_turn>user
When would you use an array versus a dictionary?<end_of_turn>
<start_of_turn>model
**Arrays** and **dictionaries** are both data structures used in computer programming, but they have different purposes and characteristics.
**Arrays** are ordered collections of elements of the same data type. They are indexed by their position in the array, starting from 0. Arrays are ideal for storing and accessing data in a sequential manner.
**Dictionaries**, on the other hand, are unordered collections of key-value pairs. Keys are unique identifiers that are used to access the corresponding values. Dictionaries are ideal for storing and accessing data based on a specific key.
**When to use an array:**
* When you need to store a fixed number of elements of the same data type.
* When you need to access elements by their position in the array.
* When you need to iterate over the elements in a specific order.
**When to use a dictionary:**
* When you need to store a variable number of elements of different data types.
* When you need to access elements by their key.
* When you need to quickly check if a key exists in the dictionary.
**Example:**
**Array:**
# Create an array of integers
numbers = [1, 2, 3, 4, 5]
# Access the first element
print(numbers[0]) # Output: 1
# Iterate over the elements
for number in numbers:
print(number)
**Dictionary:**
# Create a dictionary of names and ages
names_ages = {"John": 30, "Jane": 25, "Peter": 40}
# Access the age of John
print(names_ages["John"]) # Output: 30
# Check if a key exists
if "Mary" in names_ages:
print("Mary is in the dictionary")
else:
print("Mary is not in the dictionary")
**In summary:**
* Use an array when you need to store a fixed number of elements of the same data type and access them by their position.
* Use a dictionary when you need to store a variable number of elements of different data types and access them by their key.<end_of_turn>
Technical Details
CodeGemma is based on the Gemma 7b model with additional training on web documents, mathematics, and code, with a mixture of 80% code and 20% natural language.
The code used is based on publicly avaialble code repositories.
The instruct version was further trained on mathematical datasets in an attempt to improve its mathematical reasoning capabilities, as well as synthetic code generation combined with a second LLM for evaluation and reinforcement feedback.
Additional details can be found on Google's official report PDF here
Special thanks
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for these quants, which improves the overall quality!
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