codellama-7b is in 4 bit format with 512 output token size limit

Introduction

CodeLlama-7B is a natural language processing model that specializes in code assistant and generation applications. It is part of the Code Llama family of models, which are open foundation models for code generation. CodeLlama-7Bis designed to interpret natural language and determine suitable options for a command-line program, providing an explanation of the solution.

CodeLlama-7BModel Details

CodeLlama-7Bis a variant of the Code Llama models family, with 7 billion parameters. It is trained using an infilling objective and fine-tuned to handle long contexts. The model is initialized with Llama 2 model weights and trained on 500 billion tokens from a code-heavy dataset.

Use Cases of CodeLlama Model

CodeLlama and its variants, including CodeLlama-Python and CodeLlama-Instruct, are intended for commercial and research use in English and relevant programming languages.

CodeLlama is a versatile language model that can be employed in various scenarios related to code generation and completion. Below are some of the primary use cases for the CodeLlama model:

• Code Completion

• CodeLlama's 7B and 13B models can be utilized for text and code completion. Whether you need to fill in code or text, you can achieve this using the model.

• Code Infilling

• CodeLlama excels in code understanding and can generate code, including comments, that best matches a given prefix and suffix. This is particularly useful for code assistants. It is available in the base and instruction variants of the 7B and 13B models.

Dataset Information

CodeLlama-7B is trained on a code-heavy dataset of 500 billion tokens. The dataset is not specified in the available source. 

Evaluation

CodeLlama-7Bhas been evaluated on major code generation benchmarks, including HumanEval, MBPP, and APPS, as well as a multilingual version of HumanEval (MultiPL-E). The model has established a new state-of-the-art amongst open-source LLMs of similar size. Notably, CodeLlama-7B outperforms larger models such as CodeGen-Multi or StarCoder, and is on par with Codex. 

Advantages

• State-of-the-art performance: CodeLlamahas established a new state of the art amongst open-source LLMs, making it a valuable tool for developers and researchers alike.
•  Safer deployment: CodeLlama is designed to be safer to use for code assistant and generation applications, making it a valuable tool for tasks that require natural language interpretation and safer deployment. 
• Instruction following: CodeLlama is particularly useful for tasks that require instruction following, such as infilling and command-line program interpretation. 
• Large input contexts: CodeLlama supports large input contexts, making it a valuable tool for programming tasks that require natural language interpretation and support for long contexts. 
• Fine-tuned for instruction following: CodeLlama is fine-tuned with an additional approximately 5 billion tokens to better follow human instructions, making it a valuable tool for tasks that require natural language interpretation and instruction following.

Limitations

The dataset used to train CodeLlama-7B is not specified in the available source. Additionally, while the model has established a new state of the art amongst open-source LLMs, its performance may be limited in certain contexts or for certain programming tasks.