Code Llama, a state-of-the-art language model (LLM), has been released to assist developers in generating and discussing code. With the potential to enhance efficiency, lower barriers to entry for coding learners, and promote robust software development, Code Llama aims to revolutionize the way programmers work.
Developed under an open approach to artificial intelligence (AI), Code Llama is made available for both research and commercial use under the same community license as its predecessor, Llama 2. By adopting this open model, the development of innovative, safe, and responsible AI tools is encouraged.
Code Llama is an LLM specialized in code generation, achieved through additional training on code-specific datasets and extended sampling. As a result, it possesses enhanced coding capabilities, allowing it to generate code and natural language about code based on both code and natural language prompts. It also serves as a valuable resource for code completion and debugging, supporting popular programming languages such as Python, C++, Java, and more.
The release of Code Llama comprises three sizes with varying capacities based on parameters: 7B, 13B, and 34B. These models have been trained with an extensive dataset of 500B tokens of code and code-related information. The smaller 7B and 13B models are designed for tasks requiring low latency, such as real-time code completion, while the 34B model offers the highest-quality results and improved coding assistance.
Additionally, two further variations of Code Llama have been fine-tuned to cater to specific needs. Code Llama – Python has undergone extensive training on 100B tokens of Python code, making it suitable for language-specific code generation. On the other hand, Code Llama – Instruct has been aligned to focus on understanding natural language instructions and generating helpful and safe answers, making it ideal for code generation with a human-centric approach.
The introduction of LLMs, like Code Llama, aims to streamline developer workflows automating repetitive tasks. The open approach to AI tools, specifically those specialized in coding, fosters innovation and safety within the community. The availability of publicly accessible, code-specific models such as Code Llama allows for thorough evaluation, issue identification, and vulnerability resolution.
Code Llama is poised to support software engineers across various sectors, including research, industry, open-source projects, NGOs, and businesses. However, there are countless untapped use cases waiting to be explored. By providing Code Llama alongside Llama 2, it is hoped that developers will be inspired to create innovative tools for both research and commercial products.
Learn more about Code Llama on our AI blog or download the Code Llama model.
– Language Model (LLM): A language model is an AI model designed to understand and generate natural language.
– Code Generation: The process of automatically creating executable code based on certain input, such as prompts or instructions.
– Dataset: A collection of data used to train an AI model.
– Parameters: In the context of AI models, parameters refer to the variables that the model uses to adjust its behavior.
– Code Completion: The automated suggestion or completion of code snippets based on context or user input.
– Latency: The time delay between a user’s action and the model’s response.
– Original article: [source]
– AI blog: [source]