Tool Information
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.
F.A.Q (20)
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.
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.
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.
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.
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.
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.
Code Llama supports many popular programming languages in use today, including Python, C++, Java, PHP, Typescript (Javascript), C#, and Bash.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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