Code Llamaのプロンプト方法

Two weeks ago the Code Llama model was released by Meta with three variations:

This guide walks through the different ways to structure prompts for Code Llama for its different variations and features.

Examples below use the 7 billion parameter model with 4-bit quantization, but 13 billion and 34 billion parameter models were made available as well. This guide uses the open-source Ollama project to download and prompt Code Llama, but these prompts will work in other model providers and runtimes too.

The instruct model was trained to output human-like answers to questions. This model variation is the easiest to use and will behave closest to ChatGPT, with answer questions including both natural language and code:

ollama run codellama:7b-instruct 'You are an expert programmer that writes simple, concise code and explanations. Write a python function to generate the nth fibonacci number.' Response

Here is a Python function that generates the nth Fibonacci number: def fib(n): if n return result With Code Llama, infill prompts require a special format that the model expects.

{prefix} {suffix} To use this with existing code, split the code before and after in the example above the into parts: the prefix, and the suffix. For example, for our LCM example above:

ollama run codellama:7b-code ' def compute_gcd(x, y): return result ' While results will vary, you should get something like this:

if x == y: return x if x > y: x = x - y else: y = y - x result = compute_gcd(x, y) Note: the model may return at the end of the result. This is a special token in the response that represents the end of the response similar to , and

As a thank you to the community and tooling that created the model, the authors of Code Llama included a Python variation which is fine-tuned on 100B additional Python tokens, making it a good model to use when working on machine learning-related tooling, or any other Python code:

ollama run codellama:7b-python ' # django view for rendering the current day and time without a template def current_datetime(request):' Response

now = datetime.now() html = "It is now %s." % now return HttpResponse(html) Tools built on Code Llama

Cody has an experimental version that uses Code Llama with infill support.

Continue supports Code Llama as a drop-in replacement for GPT-4

Fine-tuned versions of Code Llama from the Phind and WizardLM teams

Open interpreter can use Code Llama to generate functions that are then run locally in the terminal