Code Llama is a powerful extension to Llama 2 that offers advanced coding solutions. It can be installed locally on your computer using the Text Generation Web UI application. Here are the steps to install Code Llama on your local computer:
- 1. Download the Code Llama model from Meta AI's blog post for Llama Code or from Hugging Face, a user who regularly updates the models .
- 2. Install the Text Generation Web UI application on your computer.
- 3. Open the Text Generation Web UI application and select the Code Llama model.
- 4. Start coding with Code Llama!
Code Llama has several features that make it a versatile tool for both novice and experienced coders alike. These include:
- - State-of-the-Art Performance: Code Llama has been benchmarked to deliver top-tier results among all open-source language models for coding.
- - Infilling Capabilities: These models possess the unique ability to infill or complete parts of the code by intuitively understanding the surrounding context.
- - Support for Large Input Contexts: Code Llama can efficiently handle and process extended input contexts, ensuring that even long segments of code are interpreted accurately.
- - Zero-Shot Instruction Following: This feature empowers Code Llama to comprehend and follow instructions for programming tasks without any prior specific training on them.
Code Llama is a product of meticulous fine-tuning from Llama 2's base models. It comes in three distinct flavors: Vanilla, Instruct, and Python, each offering unique features to cater to different coding needs.
Here is the Python code for each step:
1. Downloading the model:
import requests
url = "https://huggingface.co/meta-code/CodeLlama/resolve/main/config.json"
response = requests.get(url)
model_url = response.json()["model"]["url"]
model_name = model_url.split("/")[-1]
model_path = f"./{model_name}"
model_response = requests.get(model_url)
with open(model_path, "wb") as f:
f.write(model_response.content)
!pip install textgenrnn
from textgenrnn import textgenrnn
model = textgenrnn(model_path)
model.generate(1, temperature=0.5)
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