Showing 8 open source projects for "programming without coding"

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  • Simply solve complex auth. Easy for devs to set up. Easy for non-devs to use. Icon
    Simply solve complex auth. Easy for devs to set up. Easy for non-devs to use.

    Transform user access with Frontegg CIAM: login box, SSO, MFA, multi-tenancy, and 99.99% uptime.

    Custom auth drains 25% of dev time and risks 62% more breaches, stalling enterprise deals. Frontegg platform delivers a simple login box, seamless authentication (SSO, MFA, passwordless), robust multi-tenancy, and a customizable Admin Portal. Integrate fast with the React SDK, meet compliance needs, and focus on innovation.
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    Build Securely on AWS with Proven Frameworks

    Lay a foundation for success with Tested Reference Architectures developed by Fortinet’s experts. Learn more in this white paper.

    Moving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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  • 1
    DeepSeek R1

    DeepSeek R1

    Open-source, high-performance AI model with advanced reasoning

    DeepSeek-R1 is an open-source large language model developed by DeepSeek, designed to excel in complex reasoning tasks across domains such as mathematics, coding, and language. DeepSeek R1 offers unrestricted access for both commercial and academic use. The model employs a Mixture of Experts (MoE) architecture, comprising 671 billion total parameters with 37 billion active parameters per token, and supports a context length of up to 128,000 tokens. DeepSeek-R1's training regimen uniquely...
    Downloads: 63 This Week
    Last Update:
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  • 2
    Qwen3-Coder

    Qwen3-Coder

    Code version of Qwen3, the large language model by Alibaba Cloud

    ... Claude Sonnet. Qwen3-Coder supports an exceptionally long context window of 256,000 tokens, extendable to 1 million tokens using Yarn, enabling repository-scale code understanding and generation. 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.
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    Downloads: 15 This Week
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  • 3
    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...
    Downloads: 3 This Week
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  • 4
    bloom

    bloom

    Multilingual 176B language model for text and code generation tasks

    BLOOM (BigScience Large Open-science Open-access Multilingual Language Model) is a 176-billion parameter autoregressive language model developed by the BigScience Workshop. It generates coherent text in 46 natural languages and 13 programming languages, making it one of the most multilingual LLMs publicly available. BLOOM was trained on 366 billion tokens using Megatron-DeepSpeed and large-scale computational resources. It can perform various tasks via prompt-based learning, even without task...
    Downloads: 0 This Week
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  • AI-powered service management for IT and enterprise teams Icon
    AI-powered service management for IT and enterprise teams

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    Give your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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  • 5
    DeepSeek-R1-0528

    DeepSeek-R1-0528

    DeepSeek-R1-0528 is a powerful reasoning-focused LLM with 64K context

    DeepSeek-R1-0528 is an upgraded large language model developed by DeepSeek AI, designed to improve deep reasoning, inference, and programming capabilities. With a context length of up to 64K tokens and 685 billion parameters, it introduces enhanced algorithmic optimizations and expanded token usage per task. Compared to previous versions, it significantly improves benchmark scores in math (e.g., AIME 2025: 87.5%), logic, and coding tasks like LiveCodeBench and SWE Verified. It supports system...
    Downloads: 0 This Week
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  • 6
    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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  • 7
    Devstral

    Devstral

    Agentic 24B LLM optimized for coding tasks with 128k context support

    ..., compatible with frameworks like vLLM, Transformers, llama.cpp, and Ollama. It is licensed under Apache 2.0 and is fully open for commercial and non-commercial use. Its Tekken tokenizer allows a 131k vocabulary size for high flexibility in programming languages and natural language inputs. Devstral is the preferred backend for OpenHands, where it acts as the reasoning engine for autonomous code agents.
    Downloads: 0 This Week
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  • 8
    DeepSeek-V3-0324

    DeepSeek-V3-0324

    Advanced multilingual LLM with enhanced reasoning and code generation

    DeepSeek-V3-0324 is a powerful large language model by DeepSeek AI that significantly enhances performance over its predecessor, especially in reasoning, programming, and Chinese language tasks. It achieves major benchmark improvements, such as +5.3 on MMLU-Pro and +19.8 on AIME, and delivers more executable, aesthetically improved front-end code. Its Chinese writing and search-answering capabilities have also been refined, generating more fluent, contextually aware long-form outputs. Key...
    Downloads: 0 This Week
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