Codefuse-ai

Models by this creator

CodeFuse-CodeLlama-34B

codefuse-ai

Total Score

92

The CodeFuse-CodeLlama-34B is a 34 billion parameter code-focused large language model (LLM) developed by codefuse-ai. This model is a fine-tuned version of the CodeLlama-34b-Python model, trained on 600k instructions and answers across various programming tasks. It achieves state-of-the-art performance of 74.4% pass@1 on the HumanEval benchmark, outperforming other open-source models like WizardCoder-Python-34B-V1.0 and GPT-4 on this metric. Model inputs and outputs Inputs The model accepts a concatenated string of conversation data in a specific format, including system instructions, human messages, and bot responses. Outputs The model generates text continuations in response to the input prompt. Capabilities The CodeFuse-CodeLlama-34B model is highly capable at a variety of code-related tasks, including code completion, infilling, and following programming instructions. It demonstrates strong performance on benchmarks like HumanEval, indicating its ability to synthesize and understand code. The model is also a Python specialist, making it well-suited for tasks involving the Python programming language. What can I use it for? The CodeFuse-CodeLlama-34B model can be used for a wide range of applications that involve code generation, understanding, and assistance. Some potential use cases include: Building intelligent code editors or IDEs that can provide advanced code completion and suggestion capabilities. Developing chatbots or virtual assistants that can help programmers with coding tasks, answer questions, and provide code examples. Automating the generation of boilerplate code or repetitive programming tasks. Enhancing existing ML/AI systems with code-generation capabilities, such as automated machine learning pipelines or data processing workflows. Things to try One interesting thing to try with the CodeFuse-CodeLlama-34B model is to provide it with open-ended programming challenges or tasks, and observe how it approaches and solves them. The model's strong performance on benchmarks like HumanEval suggests it may be able to tackle a variety of programming problems in creative and novel ways. Developers could also experiment with fine-tuning or adapting the model for their specific use cases, leveraging the provided tools and resources from the codefuse-ai team.

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Updated 5/28/2024

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CodeFuse-DeepSeek-33B

codefuse-ai

Total Score

53

The CodeFuse-DeepSeek-33B is a 33B parameter Code-LLM (Large Language Model) that has been fine-tuned by QLoRA (Quantized Low-Rank Adaptation) on multiple code-related tasks using the base model DeepSeek-Coder-33B. The model has achieved a pass@1 (greedy decoding) score of 78.65% on the HumanEval benchmark, showcasing its strong performance in generating high-quality code. The model is part of the CodeFuse suite of code-focused AI models developed by the codefuse-ai team. Similar models in the CodeFuse lineup include CodeFuse-Mixtral-8x7B, CodeFuse-CodeGeeX2-6B, and CodeFuse-QWen-14B, all of which have shown significant improvements over their base models in code generation capabilities. Model inputs and outputs Inputs Code-related prompts**: The model takes in text-based prompts related to coding tasks, such as algorithm descriptions, function stubs, or high-level specifications. Natural language instructions**: The model can also accept natural language instructions for tasks like code generation, code completion, and code explanation. Outputs Generated code**: The primary output of the CodeFuse-DeepSeek-33B model is high-quality, contextually relevant code in a variety of programming languages. Explanations and insights**: The model can also generate natural language explanations and insights about the code, such as describing the purpose, functionality, or potential improvements. Capabilities The CodeFuse-DeepSeek-33B model has demonstrated state-of-the-art performance on code generation tasks, outperforming many other open-source language models. It is particularly adept at tasks like algorithm implementation, code completion, and code refactoring. The model's deep understanding of programming concepts and syntax allows it to generate code that is both functionally correct and idiomatic. What can I use it for? The CodeFuse-DeepSeek-33B model can be leveraged for a wide range of applications in the software development and AI research domains. Some potential use cases include: Automated programming assistance**: Integrate the model into IDEs, code editors, or developer tools to assist programmers with tasks like code generation, code completion, and code explanation. AI-powered coding tutorials**: Create interactive coding tutorials or educational content that leverage the model's ability to generate code and provide explanations. Accelerated prototyping and experimentation**: Use the model to quickly generate code prototypes or explore different algorithmic approaches, speeding up the R&D process. Intelligent code refactoring**: Leverage the model's understanding of code structure and semantics to suggest refactoring opportunities and optimize code quality. Things to try To get the most out of the CodeFuse-DeepSeek-33B model, you can experiment with providing the model with detailed prompts or instructions that capture the specific requirements of your coding tasks. Additionally, you can explore fine-tuning or adapting the model further on your own dataset or use case to further improve its performance.

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Updated 5/28/2024