Tool Information
Code Llama is an advanced tool designed to help you write and understand code more effectively.
Imagine having a powerful assistant at your fingertips that can generate code and explain it in plain language—that’s exactly what Code Llama does. Built on the Llama 2 foundation, it comes in three models: the standard Code Llama, Code Llama - Python, which focuses specifically on Python coding, and Code Llama - Instruct, fine-tuned to interpret natural language instructions.
With Code Llama, you can use both code and plain language prompts to achieve various tasks like code completion and debugging. It supports several popular programming languages, including Python, C++, Java, PHP, TypeScript, C#, and Bash. The models are available in different sizes—7 billion parameters, 13 billion, and even 34 billion—meaning you can choose one that fits your needs perfectly. The 7B and 13B models are great for filling in gaps when you’re coding, while the 34B model offers the most comprehensive coding assistance, although it might take a bit longer to respond.
These models can handle input sequences up to 100,000 tokens long, which means they can keep track of extensive code contexts, making code generation and debugging much more relevant and effective. Plus, Code Llama has two specialized versions: one for Python code generation and another that provides safe, helpful answers when you ask questions in natural language. Just keep in mind that Code Llama is really focused on coding tasks and isn't meant for general natural language queries.
It’s also worth mentioning that Code Llama has been put through its paces against other open-source language models and has shown impressive results, especially on coding challenges like HumanEval and Mostly Basic Python Programming (MBPP). The development team has placed a strong emphasis on safety and responsible use while creating this tool.
In a nutshell, Code Llama is a versatile and effective resource that can streamline your coding experience, assist developers, and help those learning to code understand it better. It’s here to enhance your coding journey!
∞Pros and Cons
Pros
- can complete code
- fine-tuned for understanding natural language instructions
- supports Python
- C#
- 13B
- handles input sequences up to 100
- designed for code-specific tasks
- increases software consistency
- serves as an educational tool
- 34B
- can insert code into existing code
- different models: 7B
- understands code
- the 34B model provides better coding assistance
- 13B
- suitable for lengthy input sequences for complex programs
- has potential to lower the barrier for code learners
- has high safety measures
- the 7B model can be served on a single GPU
- provides details on model limitations and known challenges
- open for community contributions
- Java
- supports real-time code completion
- offers more context from the codebase for relevant generations
- may evaluate risks
- stable generations
- has a specialized Python model
- can accommodate new tools for research and commercial products.
- Generates code
- facilitates development of new technologies
- Typescript
- supports debugging tasks
- training recipes available on Github
- scored high on HumanEval and MBPP benchmarks
- 000 tokens
- outlines measures for addressing input- and output-level risks
- outperformed other open-source LLMs
- PHP
- the 7B and 13B models come with fill-in-the-middle (FIM) capability
- the instruction variant is better at understanding human prompts
- 34B
- provides large token context for intricate debugging
- C++
- useful for evaluating and improving performance
- safer in generating responses
- helpful for defining content policies and mitigation strategies
- free for research and commercial use
- includes a Responsible Use Guide
- model weights publicly available
- the Python variant is fine-tuned with 100B tokens of Python code
- Bash
- available in three sizes: 7B
Cons
- Doesn't always provide safe answers
- Needs users to follow licensing and policy rules
- Not suitable for language tasks
- May create harmful or risky code
- Special models needed for specific languages
- Needs a lot of tokens
- Higher delay with 34B model
- Service and delay needs differ between models
- Doesn't handle general language tasks well
- Not flexible for non-coding tasks
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