codellama-7b-instruct in 4 bit format.

Important Instructions

The Code Llama model was fine-tuned for chat using a specific structure for prompts, relying on the following special tokens:

<s> - the beginning of the entire sequence.
<<SYS>> - the beginning of the system message.
<</SYS>> - the end of the system message.
[INST] - the beginning of some instructions
[/INST] - the end of the instructions

Introduction

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

CodeLlama-7B-Instruct Model Details

CodeLlama-7B-Instruct is 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.

• Conversational Instructions

• CodeLlama's base model can be employed for both completion and infilling, as mentioned earlier. Additionally, there's an instruction fine-tuned model for use in conversational interfaces. To prepare inputs for this task, prompt template need to be use. Here's an example of a prompt template:

<s>[INST] <<SYS>>
{{ system_prompt }}
<</SYS>>

{{ user_msg_1 }} [/INST] {{ model_answer_1 }} </s><s>[INST] {{ user_msg_2 }} [/INST]

Dataset Information

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

Evaluation

CodeLlama-7B-Instruct has 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: CodeLlama-Instruct has established a new state of the art amongst open-source LLMs, making it a valuable tool for developers and researchers alike.
•  Safer deployment: CodeLlama-Instruct 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-Instruct is particularly useful for tasks that require instruction following, such as infilling and command-line program interpretation. 
• Large input contexts: CodeLlama-Instruct 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-Instruct 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-Instruct 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.