Coding 2023-08-24
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Code Llama

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Enhanced coding with code generation and understanding.
Generated by ChatGPT

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


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


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


What is Code Llama?
How does Code Llama generate code?
What are the three different models of Code Llama?
How is Code Llama specialized for Python?
What is the key function of Code Llama - Instruct?
How can Code Llama be used for code completion?
What programming languages does Code Llama support?
What is the relevance of 7B,13B, and 34B with Code Llama models?
What is the maximum input sequences Code Llama can handle?
What does fill-in-the-middle capability mean in Code Llama?
Is Code Llama suitable for general natural language tasks?
How does Code Llama score on coding benchmarks such as HumanEval and Mostly Basic Python Programming (MBPP)?
In what ways is Code Llama a potential productivity and educational tool?
How is Code Llama a more innovative, safe, and responsible AI tool?
How does Code Llama aid in debugging scenarios?
Why is Code Llama not recommended for general natural language tasks?
What safety measures were undertaken in the development of Code Llama?
How can one leverage Llama 2 to create new innovative tools?
Why is Code Llama released under the same community license as Llama 2?
How different is Code Llama from Llama 2?

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