Hugging Face - Part2 - Code generation with Code Llama - Instruct

Code Llama, an impressive publicly available machine learning model, is a specialised version of Llama 2 that was created by further training Llama 2 on code-specific datasets. It is specifically designed to tackle coding challenges. It can generate both code and descriptive natural language about code, making it a versatile asset for developers. Some common use cases include writing new functions or even debugging existing code. It supports a wide range of popular programming languages, including Python, C++, Java, PHP, Typescript (Javascript), C#, and Bash.

Code Llama – Instruct, an advanced variation of Code Llama which is designed to accept natural language instructions as input and returns the expected output. This unique feature makes the model more adept at understanding and fulfilling user requirements. The Meta AI team recommend using Code Llama - Instruct variants whenever you intend to use Code Llama for your code generation tasks.

In this blog post, I will guide you through the process of employing the Code Llama - Instruct model from Hugging Face locally for code generation tasks. We will be utilizing the Python Transformers library for this. You can access the Python script in my GitHub repository:

https://github.com/vineethac/huggingface/tree/main/3-codellama-instruct

Example

root@hf-5:/# python3 codellama_prompt.py
tokenizer_config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 749/749 [00:00<00:00, 3.44MB/s]
tokenizer.model: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 500k/500k [00:00<00:00, 4.12MB/s]
tokenizer.json: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.84M/1.84M [00:00<00:00, 9.76MB/s]
special_tokens_map.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 411/411 [00:00<00:00, 2.08MB/s]
config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 646/646 [00:00<00:00, 3.51MB/s]
model.safetensors.index.json: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25.1k/25.1k [00:00<00:00, 47.9MB/s]
model-00001-of-00002.safetensors: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9.98G/9.98G [02:02<00:00, 81.2MB/s]
model-00002-of-00002.safetensors: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3.50G/3.50G [00:45<00:00, 76.7MB/s]
Downloading shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [02:48<00:00, 84.38s/it]
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:20<00:00, 10.10s/it]
generation_config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 116/116 [00:00<00:00, 444kB/s]


Ask codellama/CodeLlama-7b-Instruct-hf: reverse a list in python.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
Result: <s>[INST] reverse a list in python. [/INST]  There are several ways to reverse a list in Python. Here are a few methods:

1. Using the `reversed()` function:

my_list = [1, 2, 3, 4, 5]
reversed_list = list(reversed(my_list))
print(reversed_list)  # [5, 4, 3, 2, 1]

2. Using slicing:

my_list = [1, 2, 3, 4, 5]
reversed_list = my_list[::-1]
print(reversed_list)  # [5, 4, 3, 2, 1]

3. Using the `reverse()` method:

my_list = [1, 2, 3, 4, 5]
my_list.reverse()
print(my_list)  # [5, 4, 3, 2, 1]

Note that the `reverse()` method reverses the list in place, meaning that it modifies the original list. The other two methods create a new list with the elements in reverse order.

Ask codellama/CodeLlama-7b-Instruct-hf: /bye
root@hf-5:/#

The first time you execute the Python script, the model will be automatically downloaded to your local machine. Subsequently, upon subsequent runs, the previously saved model will be utilized in processing user inputs.

root@hf-5:~# cd ~/.cache/huggingface/hub/
root@hf-5:~/.cache/huggingface/hub#
root@hf-5:~/.cache/huggingface/hub# ls | grep Instruct
models--codellama--CodeLlama-7b-Instruct-hf
root@hf-5:~/.cache/huggingface/hub#
root@hf-5:~/.cache/huggingface/hub# cd models--codellama--CodeLlama-7b-Instruct-hf
root@hf-5:~/.cache/huggingface/hub/models--codellama--CodeLlama-7b-Instruct-hf# ls
blobs  refs  snapshots
root@hf-5:~/.cache/huggingface/hub/models--codellama--CodeLlama-7b-Instruct-hf# cd blobs/
root@hf-5:~/.cache/huggingface/hub/models--codellama--CodeLlama-7b-Instruct-hf/blobs#
root@hf-5:~/.cache/huggingface/hub/models--codellama--CodeLlama-7b-Instruct-hf/blobs# ls -altrh
total 13G
-rw-r--r-- 1 root root  749 Feb 19 12:03 526f464cf83353c59f7c07b9e587498b47d67a1b
-rw-r--r-- 1 root root 489K Feb 19 12:03 45ccb9c8b6b561889acea59191d66986d314e7cbd6a78abc6e49b139ca91c1e6
-rw-r--r-- 1 root root 1.8M Feb 19 12:03 6b25321d89e21832a89e6273834eab0e4378a53b
-rw-r--r-- 1 root root  411 Feb 19 12:03 d85ba6cb6820b01226ef8bd40b46bb489041c6a8
-rw-r--r-- 1 root root  646 Feb 19 12:03 8fb4018bc8ceaddbaf7d3d238911a30fd5e9081a
-rw-r--r-- 1 root root  25K Feb 19 12:03 cd3b8fb46c4d5616e91520a7a7d9a5a75af759a8
-rw-r--r-- 1 root root 9.3G Feb 19 12:05 0f52c0eab2dafa0a13e8103a426b17137f7b053e9211334158d7bd7cc1148ceb
-rw-r--r-- 1 root root 3.3G Feb 19 12:06 9ddab1824225fbe405cea67c5d8d87666f1ab5c59ec89abdf2cacae9b555da75
-rw-r--r-- 1 root root  116 Feb 19 12:06 aa9aac2cbaa80cf25094e7d9a527bd1cab9f5321
drwxr-xr-x 6 root root 4.0K Feb 19 12:06 ..
drwxr-xr-x 2 root root 4.0K Feb 19 12:06 .
root@hf-5:~/.cache/huggingface/hub/models--codellama--CodeLlama-7b-Instruct-hf/blobs#

I hope it was useful. Cheers!