Code Llama is an NLP large language model released by Meta based on LLAMA-2 and generated for **. Code Llama is a dedicated version of Llama 2 and was created by further training Llama 2 on its specific dataset.
Essentially, Code Llama has enhanced coding capabilities and is built on top of Llama 2. It can generate natural language based on and about natural language prompts (e.g., "Write me a function that outputs a Fibonacci sequence."). It can also be used for completion and commissioning. It supports many of the most popular languages in use today, including Python, C++, J**A, PHP, TypeScript (J**Ascript), C, and Bash.
Code Llama is built on top of Llama 2 and is available in three models:
code llama, the base model; Codel Llama - Python specifically targets Python's model, with further fine-tuning on Python's 100b markup. Because Python is the most benchmarking language for generation and because Python and PyTorch play an important role in the AI community. code llama - instruct, which is fine-tuned for understanding natural language instructions; 
Code Llama, with 7b, 13b, and 34b parameters, respectively. Each model is trained with 500b tokens and related data. The 7b and 13b base models and instruction models are also trained on the FIM feature, allowing them to plug into an existing one, which means they can support tasks such as out-of-the-box completion.
It is not recommended to use Code Llama or Code Llama - Python for general natural language tasks, as neither model is designed to follow natural language instructions. Code Llama is designed for specific tasks and is not intended as a base model for other tasks.
Benchmarks show that Code Llama outperforms the open-source, ****-specific, LLAMA and outperforms Llama 2. For example, Code Llama 34B scored 53. on Humaneval7% with a score of 56 on the MBPP2%, which is the highest compared to other states. State-of-the-art open solution, comparable to ChatGPT.
Although Code Llama is an open source large model, ordinary computer configurations cannot run this model at all, and to use the Code Llama model, you also need to apply to Meta official**, fortunately, NVIDIA has released**Experience version based on Code Llama, you only need to enter NVIDIA**, you can experience Code Llama for free.
After entering the experience interface, you need to wait for a while to load Code Llama, and after the Code Llama is loaded, you can happily chat with Code Llama, but Code Llama, as a large code model, is mainly used to process code information, which is not suitable for NLP tasks similar to ChatGPT.
Although as an English model, here we can still directly input Chinese for communication, and the model output is sometimes Chinese, I don't know what mechanism is here, sometimes the output is English, and then sometimes the output is Chinese. For example, the same question.
Write a bubbling sort algorithm in Python sure, I can help you with that! here's an example of how you can implement the bubble sort algorithm in python:def bubble_sort(arr): n = len(arr) for i in range(n-1): for j in range(0, n-i-1): if arr[j] >arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arrthis function takes an array as input and returns the sorted array. the algorithm works by iterating through the array and comparing adjacent elements. if an element is greater than the next element, they are swapped. this process is repeated until the array is sorted.here's an example of how you can use this function:arr = [5, 2, 8, 3, 1, 6, 4]sorted_arr = bubble_sort(arr)print(sorted_arr)this will output the sorted array: [1, 2, 3, 4, 5, 6, 8].i hope this helps! let me know if you h**e any questions or need further assistance.
Ask him to write a bubbling sorting algorithm in Python, and the answer will be in English. However, if you write a bubbling sorting algorithm in C language, the answer is in the Chinese version.
The bubble sort algorithm in C can be implemented using the following **: void bubblesort(int arr,int n) This function takes an integer array and the length of the array as arguments, and returns a sorted array. In this function, we use two nested for loops to iterate over each element in the array. In the inner loop, we compare two adjacent elements, and if they are in the wrong order, we swap their positions. In the outer loop, we repeat this process until all the elements in the array have been sorted. The time complexity of this function is o(n 2) because it requires iteration of each element in the array, and each iteration requires a comparison of adjacent elements. The spatial complexity of this function is o(1) because it only requires a temporary variable to swap the position of the element. The stability of this function is unstable because it may change the order of the same elements during the sorting process.
When I asked again and wrote a bubbling sorting algorithm using python, the model replied in Chinese, is it a matter of time, because the first question was a trial conducted a few days before writing the article, maybe the official unified upgrade.
def bubble sort(arr): n = len(arr) for i in range(n-1): for j in range(0, n-i-1): if arr[j] >arr[j+1]: arr[j], arr[j+1] = arr[j+1], arr[j] return arr This function takes an array as an argument and returns a sorted array. In this function, we use two nested for loops to iterate over each element in the array. In the inner loop, we compare two adjacent elements, and if they are in the wrong order, we swap their positions. In the outer loop, we repeat this process until all the elements in the array have been sorted. The time complexity of this function is o(n 2) because it requires iteration of each element in the array, and each iteration requires a comparison of adjacent elements. The spatial complexity of this function is o(1) because it only requires a temporary variable to swap the position of the element. The stability of this function is unstable because it may change the order of the same elements during the sorting process.
Its Chinese analysis part, the answer to the 2 questions and answers is exactly the same, in any case, with nvidia free code liama, we do not need to use our own computer to configure code liama model, for ordinary users, this ** is completely enough, and the quality of production ** can compete with chatgpt.
Experience address reference link.