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Coding 2023-08-24

Code Llama

5.0(1)

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Enhanced coding with code generation and understanding.

Overview

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Code Llama is a state-of-the-art large language model (LLM) designed specifically for generating code and natural language about code. It is built on top of Llama 2 and is available in three different models: Code Llama (foundational code model), Codel Llama - Python (specialized for Python), and Code Llama - Instruct (fine-tuned for understanding natural language instructions).

Code Llama can generate code and natural language about code based on prompts from both code and natural language inputs. It can be used for tasks such as code completion and debugging in popular programming languages like Python, C++, Java, PHP, Typescript, C#, and Bash.Code Llama comes in different sizes with varying parameters, such as 7B, 13B, and 34B.

These models have been trained on a large amount of code and code-related data. The 7B and 13B models have fill-in-the-middle capability, enabling them to support code completion tasks.

The 34B model provides the best coding assistance but may have higher latency. The models can handle input sequences of up to 100,000 tokens, allowing for more context and relevance in code generation and debugging scenarios.Additionally, Code Llama has two fine-tuned variations: Code Llama - Python, which is specialized for Python code generation, and Code Llama - Instruct, which has been trained to provide helpful and safe answers in natural language.

It is important to note that Code Llama is not suitable for general natural language tasks and should be used solely for code-specific tasks.Code Llama has been benchmarked against other open-source LLMs and has demonstrated superior performance, scoring high on coding benchmarks such as HumanEval and Mostly Basic Python Programming (MBPP).

Responsible development and safety measures have been undertaken in the creation of Code Llama.Overall, Code Llama is a powerful and versatile tool that can enhance coding workflows, assist developers, and aid in learning and understanding code.

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Code Llama was manually vetted by our editorial team and was first featured on August 27th 2023.

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Pros and Cons

Pros

Generates code

Understands code

Code completion capability

Supports debugging tasks

Supports Python, C++, Java, PHP, Typescript, C#, Bash

Different models: 7B, 13B, 34B

Handle input sequences up to 100,000 tokens

Has specialized Python model

Fine-tuned variant for understanding natural language instructions

Outperformed other open-source LLMs

Scored high on HumanEval, MBPP benchmarks

High safety measures

Free for research and commercial use

Educational tool

Three sizes available: 7B, 13B, 34B

7B and 13B models come with fill-in-the-middle (FIM) capability

Stable generations

Open for community contributions

Includes Responsible Use Guide

7B model can be served on a single GPU

34B model provides better coding assistance

Suitable for handling lengthy input sequences for complex programs

Supports real-time code completion

Designed for code-specific tasks

Can insert code into existing code

Python variant fine-tuned with 100B tokens of Python code

Instruction variant better at understanding human prompts

More context from codebase for relevant generations

Large token context for intricate debugging

Potential to lower barrier to entry for code learners

Increases software consistency

Potential risk evaluation capability

Safer generating response

Provides details of model limitations, known challenges

Facilitates development of new technologies

Training recipes available on Github

Model weights publicly available

Helpful for defining content policies and mitigation strategies

Useful for evaluating and improving performance

Outlines measures for addressing input- and output-level risks

Can accommodate new tools for research and commercial products

Cons

Higher latency with 34B model

Not suitable for natural language tasks

Doesn't generate safe responses on certain occasions

Requires user adherence to licensing and acceptable policy

May generate risky or malicious code

Specialized models required for specific languages

Does not perform general natural language tasks

Requires a large volume of tokens

Lacks adaptability for non-coding tasks

Service and latency requirements vary between models

Q&A

What is Code Llama?

Code Llama is a state-of-the-art large language model designed specifically for generating code and natural language about code. It is built on top of Llama 2, enhancing coding capabilities. It is available in three models: the foundational code model, a version specialized for Python, and one fine-tuned for understanding natural language instructions. The model can generate code given text prompts, aiding in tasks like code completion and debugging.

How does Code Llama generate code?

Code Llama generates code based on prompts from both code and natural language inputs. It utilizes its extensive language model, trained on a wealth of code and code-related information, to comprehend the provided prompt and produce relevant code in return. It's designed to insert code into existing code, making it suitable for tasks like code completion.

What are the three different models of Code Llama?

The three models of Code Llama are: Code Llama, the foundational code model, that generates code and natural language based on prompts from both code and natural language inputs; Code Llama - Python, which is specialized for Python code generation; and Code Llama - Instruct, which is fine-tuned for understanding natural language instructions, providing helpful and safe answers in natural language.

How is Code Llama specialized for Python?

The Code Llama - Python is specialized for Python by being further fine-tuned on 100B tokens of Python code. This fine-tuning allows it to generate Python code according to the most benchmarked language for code generation providing additional utility.

What is the key function of Code Llama - Instruct?

Code Llama - Instruct's key function is understanding natural language instructions. It's been trained with a different objective, providing it with a natural language instruction input and the expected output. This training makes it capable of better understanding what users want from their prompts.

How can Code Llama be used for code completion?

Code Llama supports code completion through its fill-in-the-middle capability. This enables it to insert new code into existing code, making it an ideal tool for code completion tasks. Based on the code and natural language prompts provided to it, Code Llama generates code that fills in the gaps.

What programming languages does Code Llama support?

Code Llama supports many popular programming languages in use today, including Python, C++, Java, PHP, Typescript (Javascript), C#, and Bash.

What is the relevance of 7B,13B, and 34B with Code Llama models?

The relevance of 7B, 13B, and 34B with the Code Llama models pertains to the varying parameters of different sizes in which Code Llama comes. These figures represent the number of parameters in the models. The higher the number, the more advanced the model with the 34B model offering the best coding assistance, but potentially with a higher latency.

What is the maximum input sequences Code Llama can handle?

Code Llama can handle input sequences of up to 100,000 tokens. This allows for more context and relevance in code generation and debugging scenarios, enabling generation of longer programs and extracting meaningful context from larger codebases.

What does fill-in-the-middle capability mean in Code Llama?

Fill-in-the-middle capability in Code Llama refers to the model's ability to insert code into existing code during tasks like code completion. This essentially means that it can fill in missing pieces or extend code based on the existing context without having to rewrite or restructure the entire codebase.

Is Code Llama suitable for general natural language tasks?

Code Llama is not suitable for general natural language tasks. Its primary function and focus is on code-specific tasks, and neither Code Llama nor Code Llama - Python models are designed to follow general natural language instructions.

How does Code Llama score on coding benchmarks such as HumanEval and Mostly Basic Python Programming (MBPP)?

Code Llama has demonstrated superior performance on coding benchmarks such as HumanEval and Mostly Basic Python Programming (MBPP). For instance, Code Llama 34B scored 53.7% on HumanEval and 56.2% on MBPP, outperforming other state-of-the-art open solutions.

In what ways is Code Llama a potential productivity and educational tool?

Code Llama aids productivity and education by allowing developers to generate code and natural language about code from both code and natural language prompts. This can speed up workflows, help in new software creation and debug existing code. For learners, it can lower the barrier to entry for people who are just starting to code.

How is Code Llama a more innovative, safe, and responsible AI tool?

Code Llama is considered more innovative, safe, and responsible as it is developed with extensive safety measures including red teaming efforts and quantitative evaluation of the risk of generating malicious code. It has also been developed in an open approach to encourage innovation and safety in its usage.

How does Code Llama aid in debugging scenarios?

In debugging scenarios, Code Llama can be useful in handling large chunks of code. With its ability to take in input sequences of up to 100,000 tokens, developers can provide the model with more context from the codebase to make the generations more relevant, assisting in debugging larger codebases.

Why is Code Llama not recommended for general natural language tasks?

Code Llama is not recommended for general natural language tasks because it is specialized for code-specific tasks. Its capabilities and tuning are specifically geared towards understanding and generating code, not general natural language progression.

What safety measures were undertaken in the development of Code Llama?

Numerous safety measures were undertaken in the development of Code Llama. These include a quantitative evaluation of Code Llama’s risk of generating malicious code, and the examination of responses to prompts that attempted to solicit malicious code, thereby ensuring safer responses.

How can one leverage Llama 2 to create new innovative tools?

Llama 2 can be leveraged to create new innovative tools by developing specialized versions like Code Llama, which enhances coding capabilities. By further training Llama 2 on specific datasets, it's possible to create more specialized models suitable for various tasks.

Why is Code Llama released under the same community license as Llama 2?

Code Llama is released under the same community license as Llama 2 to facilitate the development of new technologies that improve people's lives and to make it available for both non-commercial and commercial use. The hope is that by being openly available, the entire community can evaluate its capabilities, recognize issues, and fix vulnerabilities.

How different is Code Llama from Llama 2?

Code Llama is a variant of Llama 2 that specifically focuses on code. While it's built on top of Llama 2, Code Llama was further trained on code-specific datasets, giving it enhanced coding capabilities. Its variants, including those specialized for Python or fine-tuned for understanding natural language instructions, offer capabilities beyond those of the base Llama 2 model.