This week MetaAI has officially unveiled Code Llama, a revolutionary extension to Llama 2, designed to cater to coding needs. This innovative tool is now available to download and install locally and bridges the gap between the GPT’s 3.5 model and Llama, offering a more advanced and efficient coding solution.
Named “Code Llama”, these models draw their lineage from the Llama 2 framework. They set new standards in performance metrics, especially when compared to other models available in the open domain. Meta has chosen to release Code Llama under a permissive license. This ensures that enthusiasts, researchers, and businesses alike can leverage these models for both academic research and commercial applications without any restrictions.
Key Features of Code Llama:
- State-of-the-Art Performance: Code Llama has been benchmarked to deliver top-tier results among all open-source language models for coding.
- Infilling Capabilities: These models possess the unique ability to infill or complete parts of the code by intuitively understanding the surrounding context.
- Support for Large Input Contexts: Code Llama can efficiently handle and process extended input contexts, ensuring that even long segments of code are interpreted accurately.
- Zero-Shot Instruction Following: This feature empowers Code Llama to comprehend and follow instructions for programming tasks without any prior specific training on them.
How to install Code Llama locally
Code Llama is not just a coding tool; it’s a coding powerhouse. It is capable of debugging, generating code, and focusing on natural language about code. This makes it a versatile tool for both novice and experienced coders alike.
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In a head-to-head comparison with the GPT’s 3.5 model, Code Llama’s Python model emerged victorious, scoring a remarkable 53.7 in the human evaluation benchmark. This is a significant leap from the GPT’s 3.5 model score of 48.1, demonstrating Code Llama’s superior capabilities.
One of the most appealing aspects of Code Llama is its accessibility. It is fully open source and free for both research and commercial use. This means that anyone, anywhere, can take advantage of its advanced features without any financial constraints.
Code Llama is a product of meticulous fine-tuning from Llama 2’s base models. It comes in three distinct flavors: Vanilla, Instruct, and Python, each offering unique features to cater to different coding needs. The model sizes range from 7 billion parameters to a whopping 34 billion. Even the smallest models can be run on a local desktop with decent GPUs, making Code Llama a highly accessible tool for all.
Installing Code Llama is a breeze. It can be installed locally on a desktop using the Text Generation Web UI application. The model can be downloaded from Meta AI’s blog post for Llama Code or from Hugging Face, a user who regularly updates the models. Code Llama is designed to cater to a wide range of users. It comes in different parameter sizes, and users with less powerful GPUs are advised to stick with the 7 billion parameter models. Once loaded onto the Text Generation Web UI, users can start chatting with the model right away.
Code Llama’s proficiency is a result of rigorous training on 500 billion tokens during the initial phase. The training predominantly used near duplicates data sets of publicly accessible code. Interestingly, only 8% of the sample data comes from natural language data sets related to code. This model is not just proficient in understanding natural language nuances; it excels at it. It can generate detailed responses from code input prompts, making it a highly efficient and effective tool for coders. With Code Llama, the future of coding looks bright and promising.
Meta understands that developers and researchers have diverse requirements. Hence, they’ve rolled out various versions of Code Llama to cater to different needs:
- Foundation Models (Code Llama): The basic version that’s suitable for a broad spectrum of coding tasks.
- Python Specializations (Code Llama – Python): For those who are specifically working in the Python ecosystem, this model offers specialized capabilities.
- Instruction-Following Models (Code Llama – Instruct): Ideal for tasks that require following specific programming instructions.
Specifications and Performance Metrics:
- The models come in three sizes based on the number of parameters: 7B, 13B, and 34B.
- All the models have been trained to process sequences as long as 16k tokens. Their efficiency is evident as they demonstrate improvements even on input lengths that extend to 100k tokens.
- Both the 7B and 13B versions of Code Llama and Code Llama – Instruct can perform infilling based on the context of the content.
- On specific code benchmarks like HumanEval and MBPP, Code Llama models have recorded impressive scores of 53% and 55%, respectively. This establishes their superiority in the open model domain.
- A significant highlight is the performance of the Code Llama – Python 7B variant. It surpasses the performance of the Llama 2 70B model on both HumanEval and MBPP benchmarks.
- Across the board, all the Code Llama variants have been observed to outshine every other publicly available model on the MultiPL-E benchmark.
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