CodeFuse-DeepSeek-33B

Maintainer: codefuse-ai

Total Score

53

Last updated 5/19/2024

👀

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

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.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

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.

Read more

Updated Invalid Date

🔮

deepseek-coder-33b-instruct

deepseek-ai

Total Score

399

deepseek-coder-33b-instruct is a 33B parameter AI model developed by DeepSeek AI that is specialized for coding tasks. The model is composed of a series of code language models, each trained from scratch on 2T tokens with a composition of 87% code and 13% natural language in both English and Chinese. DeepSeek Coder offers various model sizes ranging from 1B to 33B parameters, enabling users to choose the setup best suited for their needs. The 33B version has been fine-tuned on 2B tokens of instruction data to enhance its coding capabilities. Similar models include StarCoder2-15B, a 15B parameter model trained on 600+ programming languages, and StarCoder, a 15.5B parameter model trained on 80+ programming languages. Model inputs and outputs Inputs Free-form natural language instructions for coding tasks Outputs Relevant code snippets or completions in response to the input instructions Capabilities deepseek-coder-33b-instruct has demonstrated state-of-the-art performance on a range of coding benchmarks, including HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. The model's advanced code completion capabilities are enabled by a large 16K context window and a fill-in-the-blank training task, allowing it to handle project-level coding tasks. What can I use it for? deepseek-coder-33b-instruct can be used for a variety of coding-related tasks, such as: Generating code snippets or completing partially written code based on natural language instructions Assisting with refactoring, debugging, or improving existing code Aiding in the development of new software applications by providing helpful code suggestions and insights The flexibility of the model's different size versions allows users to choose the most suitable setup for their specific needs and resources. Things to try One interesting aspect of deepseek-coder-33b-instruct is its ability to handle both English and Chinese inputs, making it a versatile tool for developers working in multilingual environments. You could try providing the model with instructions or prompts in both languages and observe how it responds. Another interesting avenue to explore is the model's performance on more complex, multi-step coding tasks. By carefully crafting prompts that require the model to write, test, and refine code, you can push the boundaries of its capabilities and gain deeper insights into its strengths and limitations.

Read more

Updated Invalid Date

💬

deepseek-coder-33b-base

deepseek-ai

Total Score

61

deepseek-coder-33b-base is a 33B parameter model with Grouped-Query Attention trained on 2 trillion tokens, including 87% code and 13% natural language in both English and Chinese. It is part of the DeepSeek Coder series, which offers various model sizes from 1B to 33B parameters to suit different user requirements. DeepSeek Coder models have shown state-of-the-art performance on multiple programming language benchmarks like HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. Similar models in the DeepSeek Coder series include the 6.7B parameter deepseek-coder-6.7b-base, the 33B parameter deepseek-coder-33b-instruct, and the 6.7B parameter deepseek-coder-6.7b-instruct. These models differ in size and whether they have been fine-tuned on instruction data in addition to the base pretraining. Model Inputs and Outputs deepseek-coder-33b-base is a language model that can generate and complete code. It takes in text prompts as input and generates relevant code completions or continuations as output. Inputs Text prompts, such as: Code stubs or partial code snippets Natural language descriptions of desired code functionality Queries about coding concepts or algorithms Outputs Completed or generated code, such as: Filled-in code to complete a partial snippet Novel code to implement a requested functionality Explanations of coding concepts or algorithms Capabilities deepseek-coder-33b-base demonstrates advanced code generation and completion capabilities, supported by its large-scale pretraining on a vast corpus of code and text data. It can assist with a variety of coding tasks, from implementing algorithms to explaining programming constructs. For example, the model can take a prompt like "#write a quick sort algorithm" and generate a complete Python implementation of the quicksort algorithm. It can also fill in missing parts of code snippets to complete the functionality. What Can I Use It For? deepseek-coder-33b-base can be leveraged for a wide range of applications that involve programming and code generation. Some potential use cases include: Developing intelligent code editors or IDEs that offer advanced code completion and generation features Building chatbots or virtual assistants that can engage in dialog about coding and provide programming help Automating repetitive coding tasks by generating boilerplate code or implementing common algorithms Enhancing software development productivity by assisting programmers with coding tasks The model's scalability and strong performance make it well-suited for commercial use cases that require robust code generation capabilities. Things to Try One interesting aspect of deepseek-coder-33b-base is its ability to work at the repository level, generating code that is coherent and consistent with the overall context of a codebase. You can try providing the model with a larger code context, such as imports, function definitions, and other supporting code, and see how it generates new functionality that seamlessly integrates with the existing structure. Another area to explore is the model's handling of more complex coding challenges, such as implementing data structures and algorithms. You can provide it with prompts that require reasoning about edge cases, optimizations, and other advanced programming concepts to see the depth of its capabilities.

Read more

Updated Invalid Date

🏅

deepseek-coder-1.3b-base

deepseek-ai

Total Score

55

deepseek-coder-1.3b-base is a 1.3 billion parameter AI model developed by deepseek-ai that is specialized in code generation and completion. It was trained from scratch on 2 trillion tokens, with 87% of the data being code and the remaining 13% being natural language data in both English and Chinese. Compared to the deepseek-coder-33b-base and deepseek-coder-6.7b-base models, the 1.3 billion parameter version is more lightweight and accessible, while still providing state-of-the-art performance on multiple programming language benchmarks. Model inputs and outputs deepseek-coder-1.3b-base is a causal language model that takes in natural language or partial code as input and generates relevant text or code as output. The model can be used for a variety of code-related tasks, including code completion, code generation, and even repository-level code completion. Inputs Natural language prompts or partial code snippets Outputs Completed code snippets or generated code based on the input prompt Capabilities deepseek-coder-1.3b-base has demonstrated strong capabilities in code generation and completion, achieving state-of-the-art performance on benchmarks like HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. The model is able to understand and generate code in multiple programming languages, and can even complete complex, multi-line code segments based on partial inputs. What can I use it for? The deepseek-coder-1.3b-base model can be a powerful tool for developers and data scientists looking to streamline their coding workflows. Some potential use cases include: Generating boilerplate code or scaffolding for new projects Completing partially written code snippets to save time Generating code to implement specific algorithms or functionality Assisting with code refactoring and optimization Aiding in the onboarding of new developers by providing example code Things to try One interesting capability of deepseek-coder-1.3b-base is its ability to perform "repository-level" code completion, where the model can generate relevant code based on the context of an entire codebase, rather than just a single code snippet. This can be particularly useful for tasks like implementing common design patterns or integrating third-party libraries into a project. Another aspect to explore is the model's performance on domain-specific coding tasks, such as data analysis, machine learning, or web development. The model's strong natural language understanding may enable it to generate high-quality code for a variety of use cases beyond general-purpose programming.

Read more

Updated Invalid Date