Search Results for "coding" - Page 3
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Showing 76 open source projects for "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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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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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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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. -
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MiMo-V2.5
Omnimodal AI model for agents, coding, and long-context tasks
...With a context window of up to 1 million tokens, it can handle large documents, extended conversations, and multi-step workflows without fragmentation. MiMo-V2.5 delivers near-Pro-level performance in coding, reasoning, and agent tasks while maintaining lower cost and faster inference speeds. It also integrates advanced components such as multi-token prediction modules and specialized vision and audio encoders, making it well-suited for autonomous agents and software development. -
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GigaChat 3 Ultra
High-performance MoE model with MLA, MTP, and multilingual reasoning
...Its training corpus incorporates ten languages, enriched with books, academic sources, code datasets, mathematical tasks, and more than 5.5 trillion tokens of high-quality synthetic data. This combination significantly boosts reasoning, coding, and multilingual performance across modern benchmarks. Designed for high-performance deployment, GigaChat 3 Ultra supports major inference engines and offers optimized BF16 and FP8 execution paths for cluster-grade hardware. -
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Qwen3-Next
Qwen3-Next: 80B instruct LLM with ultra-long context up to 1M tokens
...Multi-Token Prediction (MTP) boosts both training and inference, while stability optimizations such as weight-decayed and zero-centered layernorm ensure robustness. Benchmarks show it performs comparably to larger models like Qwen3-235B on reasoning, coding, multilingual, and alignment tasks while requiring only a fraction of the training cost. -
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Ministral 3 8B Reasoning 2512
Efficient 8B multimodal model tuned for advanced reasoning tasks.
...It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling it to process both text and images for advanced reasoning tasks. This version is specifically post-trained for reasoning, making it well-suited for math, coding, and STEM applications requiring multi-step logic and problem-solving. Despite its reasoning-focused training, the model remains edge-optimized and can run locally on a single 24GB GPU in BF16, or under 12GB when quantized. It supports dozens of languages, adheres reliably to system prompts, and provides native function calling and structured JSON output—key capabilities for agentic and automation workflows. ... -
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Ministral 3 14B Reasoning 2512
High-precision 14B multimodal model built for advanced reasoning tasks
...It pairs a 13.5B-parameter language model with a 0.4B vision encoder, enabling strong multimodal reasoning across both text and images. This version is specifically post-trained for reasoning tasks, making it highly effective for math, coding, STEM workloads, and complex multi-step problem-solving. Despite its scale, the model is engineered for practical deployment and can run locally on 32GB of VRAM in BF16 or under 24GB when quantized. It maintains robust system-prompt adherence, supports dozens of languages, and provides native function calling with clean JSON output for agentic workflows. ... -
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Ministral 3 3B Reasoning 2512
Compact 3B-param multimodal model for efficient on-device reasoning
...It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require logical reasoning, analysis, or structured thinking. Despite its modest size, the model is designed for edge deployment and can run locally, fitting in ~16 GB of VRAM in BF16 or under 8 GB of RAM/VRAM when quantized. It supports dozens of languages, allowing it to function across global and multilingual contexts. ... -
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Gemopus
Stable fine-tuned Gemma model for structured, clear responses
...It also strengthens technical explanations, balancing rigor with accessibility. While not intended as a production-ready system, it serves as a high-quality local assistant for structured writing, summarization, and coding tasks. Limitations include potential hallucinations in complex domains and weaker performance compared to larger frontier models. -
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GLM-4.5-Air
Compact hybrid reasoning language model for intelligent responses
...The model is optimized for efficiency and deployment, delivering strong results across 12 industry benchmarks, with a composite score of 59.8. GLM-4.5-Air supports both English and Chinese, and is suitable for tasks involving text generation, coding, reasoning, and tool calling. Open-sourced under the MIT license, it is commercially usable and integrates with transformers, vLLM, and SGLang inference frameworks. It includes FP8 variants for faster inference and reduced memory requirements. Despite its smaller size compared to full GLM-4.5, GLM-4.5-Air maintains high performance. -
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Hy3
Open code agent for Lean 4 proofs and formal software verification
...It supports configurable reasoning effort, allowing users to disable reasoning or enable high-effort reasoning for complex prompts. This updated version of the original Leanstral focuses on performant, cost-effective formal coding and theorem-proving workflows. -
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Leanstral 1.5
Open code agent for Lean 4 proofs and formal software verification
...It supports configurable reasoning effort, allowing users to disable reasoning or enable high-effort reasoning for complex prompts. This updated version of the original Leanstral focuses on performant, cost-effective formal coding and theorem-proving workflows. -
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MiMo-V2.5-Pro
Flagship MoE model for long-context agents and complex coding
MiMo-V2.5-Pro is Xiaomi’s flagship Mixture-of-Experts (MoE) model built for the most demanding agentic, software engineering, and long-horizon reasoning tasks. It features approximately 1.02 trillion total parameters with 42B activated per inference, balancing extreme capability with efficient execution. The model supports a 1 million token context window, enabling it to maintain coherence across long workflows involving thousands of tool calls and multi-step reasoning chains.... -
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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....