Showing 7 open source projects for "no coding technology programming"

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    Qwen3-Coder

    Qwen3-Coder

    Qwen3-Coder is the code version of Qwen3

    ...It is capable of handling 358 programming languages, from common to niche, making it versatile for a wide range of development environments. The model integrates a specially designed function call format and supports popular platforms such as Qwen Code and CLINE for agentic coding workflows.
    Downloads: 13 This Week
    Last Update:
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  • 2
    CodeGeeX2

    CodeGeeX2

    CodeGeeX2: A More Powerful Multilingual Code Generation Model

    CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports...
    Downloads: 3 This Week
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  • 3
    CodeGeeX

    CodeGeeX

    CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)

    CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters, trained on 850B tokens across more than 20 programming languages. Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art...
    Downloads: 8 This Week
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  • 4
    MiniMax-M2

    MiniMax-M2

    MiniMax-M2, a model built for Max coding & agentic workflows

    MiniMax-M2 is an open-weight large language model designed specifically for high-end coding and agentic workflows while staying compact and efficient. It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real repositories and diverse programming languages. ...
    Downloads: 0 This Week
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  • 5
    Qwen2.5-Coder

    Qwen2.5-Coder

    Qwen2.5-Coder is the code version of Qwen2.5, the large language model

    Qwen2.5-Coder, developed by QwenLM, is an advanced open-source code generation model designed for developers seeking powerful and diverse coding capabilities. It includes multiple model sizes—ranging from 0.5B to 32B parameters—providing solutions for a wide array of coding needs. The model supports over 92 programming languages and offers exceptional performance in generating code, debugging, and mathematical problem-solving. Qwen2.5-Coder, with its long context length of 128K tokens, is ideal for a variety of use cases, from simple code assistants to complex programming scenarios, matching the capabilities of models like GPT-4o.
    Downloads: 4 This Week
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  • 6
    Kimi K2

    Kimi K2

    Kimi K2: 1T-param MoE model for advanced coding and agentic reasoning

    Kimi K2 (K2-Instruct-0905) is a state-of-the-art Mixture-of-Experts (MoE) language model developed by Moonshot AI, designed for high-performance reasoning, coding assistance, and agentic task orchestration. It features 1 trillion total parameters with 32 billion activated per token, enabling strong efficiency while maintaining very high capability. Kimi K2 demonstrates major gains in real-world coding and tool-use benchmarks, especially in SWE-Bench, Terminal-Bench, and multilingual programming tasks. ...
    Downloads: 0 This Week
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  • 7
    Mellum-4b-base

    Mellum-4b-base

    JetBrains’ 4B parameter code model for completions

    Mellum-4b-base is JetBrains’ first open-source large language model designed and optimized for code-related tasks. Built with 4 billion parameters and a LLaMA-style architecture, it was trained on over 4.2 trillion tokens across multiple programming languages, including datasets such as The Stack, StarCoder, and CommitPack. With a context window of 8,192 tokens, it excels at code completion, fill-in-the-middle tasks, and intelligent code suggestions for professional developer tools and IDEs....
    Downloads: 0 This Week
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