Code Llama users appreciate its 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
and Can accommodate new tools for research and commercial products, though some note a
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
and Service and latency requirements vary between models.
