Introduction

Coding language models (LLMs) are advanced artificial intelligence systems designed to assist developers in writing code more efficiently and effectively as these models are specifically trained to understand and generate code. These models leverage machine learning techniques, particularly deep learning, to learn the syntax, semantics, and structure of various programming languages. Coding LLMs have revolutionized the software development process by providing developers with powerful tools for automating repetitive tasks, accelerating prototyping, and even assisting in problem-solving.

Coding models serve as valuable aids for developers across a wide range of use cases. By understanding the nuances of various programming languages and frameworks, these models can significantly streamline the development workflow, reduce errors, and enhance productivity. Whether it's writing complex algorithms, debugging code, or exploring new libraries, coding LLMs offer versatile solutions to common programming challenges.

Overview

This Coding App Template explores various coding scenarios and includes pre-built workflows tailored to address distinct use cases, employing diverse models specialized for each unique situation.

Solve Coding Use Cases with Clarifai

Clarifai LLMs and pre-built workflows can address numerous coding use cases. Below are the most common coding use cases tackled by coding LLMs and Clarifai workflows:

1. Code Completion and Auto-Suggestion: Coding LLMs can predict and suggest code completions that can assist developers by providing context-aware code completions as they type, reducing the need to memorize syntax and API specifications. This feature is particularly useful for accelerating coding tasks and reducing errors.

• Code Completion workflow: This workflow help to predict and suggest possible code completions based on the previous context.

• Code Completion python Workflow: This workflow help to predict and suggest possible python code completions based on the previous context.

2. Code Generation from Natural Language: Developers can use coding LLMs to translate natural language descriptions of algorithms or programming tasks into executable code. This functionality simplifies the process of converting high-level requirements into working implementations, enabling faster prototyping and development.

• Code Generation Workflow: This workflow focus on generating code snippets or entire functions based on input specifications or natural language descriptions.

3. Code Summarization and Documentation: Coding LLMs can automatically generate summaries and documentation for code repositories, modules, or functions. By extracting key insights and explanations from source code, these models facilitate code understanding, collaboration, and knowledge sharing among developers.

• Code Summarisation workflow: This workflow help to concise summaries or documentation for code snippets, making it easier for developers to understand and maintain complex codebases.

4. Code Refactoring and Optimization: Developers can leverage coding LLMs to identify and refactor inefficient or redundant code segments automatically. By analyzing code patterns and performance metrics, these models suggest optimizations to improve code readability, maintainability, and performance.

• Code Refactoring workflow: This workflow can analyze existing codebases to identify opportunities for refactoring, such as removing code duplication, improving variable names, or restructuring code for better clarity.

5. Code Debugging and Error Correction: Coding LLMs assist developers in identifying and resolving syntax errors, logic bugs, and runtime exceptions in their code. By analyzing error messages, stack traces, and code context, these models provide actionable insights and suggestions for debugging code effectively.

• Code Debugging workflow: This workflow can identify common programming errors and offer suggestions for fixes. This helps developers catch bugs early in the development process and maintain code quality.

Based on your use case, you can utilize any of the above workflows to achieve accurate and precise results.

Coding Models

Coding LLMs come in various forms, each tailored to address specific use cases and challenges encountered in software development. 

Evaluation of Coding Models

To evaluate the performance of different LLMs on coding use cases, there are two important evaluation benchmarks that help to assess their performance.

• HumanEval: LLM Benchmark for Code Generation: HumanEval is the quintessential evaluation tool for measuring the performance of LLMs in code generation tasks. HumanEval consist of HumanEval Dataset and pas@k metric which use to evaluate LLM performance. This hand-crafted dataset, consisting of 164 programming challenges with unit tests, and the novel evaluation metric, designed to assess the functional correctness of the generated code, have revolutionized how we measure the performance of LLMs in code generation tasks.
• MBPP (Mostly Basic Python Programming): MBPP benchmark is designed to measure the ability of LLM to synthesize short Python programs from natural language descriptions. The Mostly Basic Programming Problems (MBPP) dataset contains 974 programming tasks, designed to be solvable by entry-level programmers covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases to check for functional correctness.

Clarifai has large number of coding LLMs which tailored to address different use cases. Some of the prominent coding LLMs include:

• deepseek-coder-33b-instruct

DeepSeek-Coder-33B-Instruct model is a SOTA 33 billion parameter code generation model, trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese and then fine-tuned on 2B tokens of instruction data, offering superior performance in code-generation, question answering and chat purpose on coding related problems.

Benchmarks

HumanEval: 75

MBBP: 70.7

• wizardCoder-Python-34B

WizardCoder is a Code Large Language Model (LLM) that has been fine-tuned on Llama2 excelling in python code generation tasks and has demonstrated superior performance compared to other open-source and closed LLMs on prominent code generation benchmarks.

Benchmarks

HumanEval: 73.2

MBBP: 63.2

Model capabilities:

• Code completion.

• Infilling.

• Instructions / chat.

• Python specialist.

• wizardCoder-15B

WizardCoder is a Code Large Language Model (LLM) that has been fine-tuned using the Evol-Instruct method. The model has been trained on a large dataset of code instruction-following tasks and has demonstrated exceptional performance on code-generation task.

Benchmarks

HumanEval: 57.3

MBBP: 51.8

Model capabilities:

• Code completion.

• Infilling.

• Instructions / chat.

• Python specialist.

• codeLlama-70b-Instruct

CodeLlama-70B-Instruct is a variant of the Code Llama models family, with 70 billion parameters, built on the Llama-2 architecture. It has been trained on a diverse set of coding languages and contexts, including Python, C++, Java, PHP, TypeScript, C#, and Bash. The model excels in code synthesis, understanding, completion, and debugging, capable of handling tasks based on both code and natural language prompts.

Benchmarks

HumanEval: 67.8

MBBP: 63.2

Model capabilities

• Code completion.

• Infilling.

• Instructions / chat.

• Python specialist.

• codeLlama-70b-Python

CodeLlama-70b-Python is the largest and best-performing model in the Code Llama family, specifically optimized for Python programming tasks. It is built on top of the Llama2 architecture and is fine-tuned for generating code and specialized in Python code generation and understanding.

Benchmarks

HumanEval: 57.3

MBBP: 6.6

Model capabilities

• Code completion.

• Infilling.

• Instructions / chat.

• Python specialist.

• claude-3-opus

Claude 3 Opus is the most capable offering within the Claude-3 family of models. It achieves state-of-the-art results on benchmark evaluations such as GPQA, MMLU, HumanEval, and MMMU. The Opus model excels in reasoning, math, and coding tasks, demonstrating increased proficiency in content creation, analysis, summarization, and handling scientific queries.

Benchmarks

HumanEval: 82.9

MBBP: 89.4

Model capabilities

• Code completion.

• Infilling.

• Instructions / chat.

• Python specialist.

• gpt-4-turbo

GPT-4 Turbo is an advanced language model with a 128K context window and perform exceptionally well coding, reasoning and math.

Benchmarks

HumanEval: 90.2

MBBP: 85.7

Model capabilities

• Code completion.

• Infilling.

• Instructions / chat.

• Python specialist.

• llama-3-70B-Instruct

Llama-3-Instruct is an advanced, scalable llm designed for diverse applications, offering state-of-the-art performance in coding, reasoning, and multi-use conversational capabilities.

Benchmarks

HumanEval: 77.4

MBBP: 82.3

Model capabilities

• Code completion.
• Infilling.
• Instructions / chat.
• Python specialist.

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