Search Results for "no coding" - Page 63
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Showing 1607 open source projects for "no coding"
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Qwable-v1
Agentic coding model combining Opus reasoning and Fable tools
Qwable-v1 is an open-weight agentic coding model created through a chained distillation process based on Qwen3.6-35B-A3B. The model combines two distinct training stages: first, it was fine-tuned on reasoning traces derived from Claude Opus 4.7 to improve structured reasoning, and then further trained on Claude Fable-5 agentic tool-use traces to develop autonomous coding and tool-calling behavior. -
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Laguna M.1
Flagship Poolside model for agentic coding and software engineering
...Laguna M.1 was designed to compete with leading frontier coding models on benchmarks such as SWE-Bench, Terminal-Bench, and other agentic engineering evaluations. It supports reasoning, tool calling, and long-context workflows, making it suitable for autonomous coding agents, software maintenance, debugging, and large-scale development projects. -
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Hy3 preview
Efficient MoE model for reasoning, coding, and AI agent workflows
...Architecturally, it uses 192 routed experts with top-8 activation, a dense-MoE hybrid design, and a native 256K-token context window. Hy3-preview is optimized for efficient deployment while maintaining strong benchmark performance across reasoning, coding, and agent evaluations. It supports function calling, integration with popular agent frameworks such as OpenClaw and OpenCode, and deployment through Transformers. -
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GLM-5.3-Flash
Efficient 320B multimodal MoE model for coding and autonomous agents
GLM-5.3-Flash is Z.ai’s natively multimodal model designed for efficient coding, agentic engineering, reasoning, and long-context workloads. It uses a sparse architecture with 320B total parameters and only 18B active parameters, targeting high capability with substantially lower inference costs. The model introduces a hybrid architecture combining sparse and linear attention to reduce long-context serving costs while retaining precise understanding across large inputs. -
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Qwen3.8-27B
Dense 27B multimodal model for coding, agents, and visual reasoning
...Thinking is enabled by default, with low, medium, and xhigh reasoning-effort settings and preserved reasoning across conversations. It also delivers substantial improvements over Qwen3.6-27B on coding and agent benchmarks while remaining deployment-friendly and compatible with Transformers, vLLM, SGLang, etc. -
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Qwen3.8-2.4T-A95B
Massive 2.4T MoE model for coding, agents, research, and reasoning
...Qwen3.8 also provides adjustable reasoning depth through low, medium, and xhigh reasoning-effort settings and preserves reasoning context across conversations. It is a text-only, thinking-first model and supports deployment through vLLM, SGLang, and TokenSpeed, with strong results across coding, tool use, research, and professional benchmarks. -
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Inkling-Small
Efficient multimodal MoE model for coding, tools, and reasoning
Inkling-Small is an open-weight general-purpose multimodal model from Thinking Machines Lab, designed for agentic systems, coding assistants, chatbots, retrieval workflows, and natural-language applications. It accepts text, images, and audio as input and produces text output, with multilingual and multi-programming-language capabilities. The model uses a sparse Mixture-of-Experts architecture with 276B total parameters and 12B active per token, enabling strong performance with lower inference cost than a fully dense model of similar scale. ... -
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DeepSeek-V4-Pro
Flagship MoE model for advanced reasoning, coding, and agents
DeepSeek-V4-Pro is a flagship open-weight Mixture-of-Experts language model designed for high-performance reasoning, coding, and agent-based workflows at scale. It features approximately 1.6 trillion total parameters with around 49B activated during inference, enabling strong efficiency while maintaining frontier-level capability. The model supports an ultra-long context window of up to 1 million tokens, making it highly suitable for long-document reasoning, large codebases, and complex multi-step tasks. ... -
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Auto-Typer-Zer0
Only Auto Typer with Text and Code typing modes to ensure perfection.
AutoTyper Zer0 - V1.0 --------------------------------- Auto Typer with Typing and Coding modes, for coding homework, and other miscellaneous uses: - Text Typing Mode: Automate plain text input seamlessly. - Code Typing Mode: Supports Python, Java, and C/C++ with options to remove comments and fix indentation. - Super Speed Mode: Type up to 100 lines in under 10 seconds. Customizable Speed & Dark Theme UI: Adjust typing speed and enjoy an aesthetic interface... -
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QuotaWake
macOS menu bar app for AI coding session readiness
QuotaWake is an open-source macOS menu bar app for AI coding session readiness and usage-window scheduling. It helps developers keep Claude Code and Codex CLI workflows ready around planned usage windows while staying local-first. QuotaWake runs on your Mac, invokes the official CLIs you already use, stores no provider tokens, makes no provider HTTP requests, and keeps readiness logs locally. -
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Altar-1
Pruned GLM-5.3 model for self-hosted cybersecurity AI and coding
...Starting from GLM-5.3’s 753B-parameter Mixture-of-Experts architecture, Altar removes 34% of routed experts using REAP, leaving 168 of 256 experts per layer and approximately 504B parameters. Eight experts remain active per token, preserving roughly 40B active parameters during inference. The model was calibrated using cybersecurity traces, coding, tool calling, reasoning, English, and multilingual Wikipedia data to preserve domain specialists during pruning. Its routed experts use INT4 W4A16 AWQ quantization, while attention, shared experts, dense layers, and the output head remain BF16. The resulting weights occupy about 328 GB and are designed for production deployment on four NVIDIA H200 GPUs through vLLM, including 128K-context workloads. -
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Qwen3.8-Flash-Next
Efficient multimodal MoE model for coding, reasoning, and AI agents
...It contains 512 MoE experts, activating 10 routed experts plus one shared expert per token. Qwen3.8-Flash-Next natively handles text, images, and video and supports a 262K-token context window extensible to 1M tokens. It targets coding, tool use, professional tasks, computer interaction, multimodal reasoning, and long-horizon agents, with configurable thinking modes and reasoning effort. -
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Ling 3.0 Tiny
Lightweight MoE model for local reasoning, coding, and AI agents
...It contains 7.9B total parameters while activating only 1.3B per token, using a hybrid architecture that alternates Kimi Delta Attention and Multi-Head Latent Attention with a sparse 128-expert MoE. The model supports both fast responses and configurable multi-step thinking, covering general agents, coding, mathematics, scientific reasoning, and instruction following. It is specifically optimized for local and resource-constrained deployment and has been validated on NVIDIA DGX Spark, Apple Silicon MacBooks, and Mac mini systems. FP8 testing reached around 100–105 tokens/s on DGX Spark and 86–90 tokens/s on an M4 Pro MacBook. BF16, FP8, and INT4 weights are available, while deployment options include SGLang, vLLM, and experimental Ollama support on Apple Silicon. -
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Nex-N2-mini
Compact agentic model for coding, tools, and productivity tasks
...Nex-N2-mini supports image-text-to-text workflows, explicit reasoning traces, robust function calling, and deployment through Transformers, vLLM, SGLang, Docker, and quantized local apps. It performs strongly across agentic, coding, search, and reasoning benchmarks, including SWE-Bench, Terminal-Bench, BrowseComp, Toolathlon, and GPQA. -
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Nex-N2-Pro
Large agentic model for coding, tools, research, and execution
...It supports image-text-to-text workflows, explicit reasoning traces, robust function calling, and deployment through Transformers, vLLM, SGLang, Docker, and quantized local apps. Nex-N2-Pro performs strongly across agentic, coding, search, and reasoning benchmarks, including Terminal-Bench, SWE-Bench Pro, BrowseComp, Toolathlon, WideSearch, GPQA Diamond, and GDPval. -
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Gemma 4 12B
Unified multimodal Gemma model for local coding and reasoning
Gemma 4 12B is Google DeepMind’s unified open-weight multimodal model designed for efficient local reasoning, coding, and multimodal understanding. Unlike other Gemma 4 models that rely on separate encoders, the 12B Unified model uses an encoder-free architecture that projects raw image patches and audio waveforms directly into the language model’s embedding space, reducing multimodal latency and simplifying fine-tuning. It supports text, image, audio, and video inputs with text output, making it useful for transcription, image understanding, video analysis, coding, and agentic workflows. ... -
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DeepSeek-V4-Flash
Efficient MoE model for million-token reasoning and coding
DeepSeek-V4-Flash is a preview Mixture-of-Experts language model built for efficient million-token context intelligence. It has 284B total parameters with 13B activated and supports a 1M-token context window, making it suitable for long-document reasoning, complex coding, agentic workflows, and large-scale information processing. The model uses a hybrid attention architecture that combines Compressed Sparse Attention and Heavily Compressed Attention to improve long-context efficiency, while Manifold-Constrained Hyper-Connections strengthen signal stability across layers. It is trained on more than 32T tokens and refined through a post-training pipeline that includes supervised fine-tuning, reinforcement learning, domain-specific expert cultivation, and on-policy distillation. ... -
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Qwen3.6-35B-A3B-FP8
FP8 Qwen model for efficient multimodal coding and agent tasks
...It is a multimodal open-weight model that combines a causal language model with a vision encoder, supporting text, image, and video inputs. Built for stability and real-world developer use, it emphasizes agentic coding, repository-level reasoning, and productive long-context workflows. A key capability is thinking preservation, which allows the model to retain reasoning traces from earlier messages, helping reduce repeated computation and improving consistency in iterative tasks. The model uses a Mixture-of-Experts design with 35B total parameters and 3B active, supports a native context window of 262,144 tokens, and can be extended to about 1,010,000 tokens with YaRN. ... -
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MiMo-V2.6-Pro
1T omnimodal MoE model for coding, agents, and long-horizon reasoning
...Its architecture combines sliding-window and global attention, a 681M-parameter vision encoder, dedicated audio encoders, and a five-layer multi-token speculative decoder. It targets coding, general and visual agents, tool use, cybersecurity, long-horizon reasoning, etc. -
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Smaug Flash
304B MoE model optimized for agentic coding and long-running workflows
...Smaug-Flash preserves the base model’s 1,048,576-token context window and low, high, and max reasoning-effort settings. Training specifically targets common agent failures such as looping, stalling, and inefficient task completion, producing substantial improvements across agentic coding benchmarks. The model uses block-FP8 attention and packed-FP4 experts and supports deployment through vLLM and SGLang while retaining compatibility with DeepSeek-V4-Flash. -
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CodingChecker
A coding style checker tool support C/C++ syntax
This project provide a framework for general coding style checker's process: 1, Support different adapter plugin for different programming language syntax 2, Support incremental scanning 3, Support customize criteria rules. 4, Generate report for scanning result. A C/C++ adapter was also implemented based on clang library as example. -
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Luna Code Checker
An advanced web-based tool for checking JavaScript code using ESLint.
Luna Code Checker An advanced web-based tool for checking JavaScript code using ESLint. How to Use Clone the repository. Run npm install to install the dependencies. Run npm start to start the server. Open http://localhost:3000 in your web browser. Write or paste your JavaScript code in the textarea. Click the "Check Code" button to see linting results. Features Comprehensive syntax and style checking using ESLint. Detailed error messages including line numbers and... -
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Qwen2-7B-Instruct
Instruction-tuned 7B language model for chat and complex tasks
Qwen2-7B-Instruct is a 7.62-billion-parameter instruction-tuned language model from the Qwen2 series developed by Alibaba's Qwen team. Built on a transformer architecture with SwiGLU activation and group query attention, it is optimized for chat, reasoning, coding, multilingual tasks, and extended context understanding up to 131,072 tokens. The model was pretrained on a large-scale dataset and aligned via supervised fine-tuning and direct preference optimization. It shows strong performance across benchmarks such as MMLU, MT-Bench, GSM8K, and Humaneval, often surpassing similarly sized open-source models. ... -
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MiniMax-M2.7
Self-evolving AI model for agents, coding, and complex workflows
MiniMax-M2.7 is a large-scale open-weight language model designed for advanced agent-based workflows, professional software engineering, and complex productivity tasks. With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30%... -
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Qwen2.5-14B-Instruct
Powerful 14B LLM with strong instruction and long-text handling
...The model supports context lengths up to 128K tokens and can generate up to 8K tokens, making it suitable for long-context applications. It demonstrates improved performance in coding, mathematics, and multilingual understanding across over 29 languages. Qwen2.5-14B-Instruct is built on a transformer backbone with RoPE, SwiGLU, RMSNorm, and attention QKV bias. It’s resilient to varied prompt styles and is especially effective for JSON and tabular data generation. The model is instruction-tuned and supports chat templating, making it ideal for chatbot and assistant use cases.
