Search Results for "asp.net-software"
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Showing 21 open source projects for "asp.net-software"
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LLamaSharp
C#/.NET binding of llama.cpp, including LLaMa/GPT model inference
The C#/.NET binding of llama.cpp. It provides APIs to infer the LLaMa Models and deploy it on the local environment. It works on both Windows, Linux and MAC without the requirement for compiling llama.cpp yourself. Its performance is close to llama.cpp. Furthermore, it provides integrations with other projects such as BotSharp to provide higher-level applications and UI. -
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GLM-5
From Vibe Coding to Agentic Engineering
...Building on earlier GLM series models, GLM-5 dramatically scales the parameter count (to roughly 744 billion) and expands pre-training data to significantly improve performance on complex tasks such as multi-step reasoning, software engineering workflows, and agent orchestration compared to its predecessors like GLM-4.5. It incorporates innovations like DeepSeek Sparse Attention (DSA) to preserve massive context windows while reducing deployment costs and supporting long context processing, which is crucial for detailed plans and agent tasks. -
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AlphaFold 3
AlphaFold 3 inference pipeline
...The system is designed for scientific research applications in structural biology, biochemistry, and bioinformatics, enabling accurate modeling of proteins, ligands, and covalent modifications. Users can perform local predictions via Docker containers, integrating AlphaFold 3’s inference process with provided JSON input configurations. The software includes flexible options for running both data preprocessing and GPU-accelerated inference, allowing users to adapt to available computational resources. -
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Qwen3.6
Qwen3.6 is the large language model series developed by Qwen team
...One of its defining goals is to enhance “agentic coding,” enabling the model to reason across entire codebases, handle multi-step development tasks, and assist with complex software engineering workflows. The architecture incorporates modern techniques such as mixture-of-experts and hybrid attention mechanisms, allowing it to scale efficiently while maintaining strong performance. -
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MiniMax-M2.1
MiniMax M2.1, a SOTA model for real-world dev & agents.
...The model is designed to be transparent, controllable, and accessible, enabling developers to build autonomous systems without relying on closed platforms. MiniMax-M2.1 excels in real-world software engineering tasks, including multilingual development and complex workflow automation. It demonstrates strong generalization across agent frameworks and consistently improves upon its predecessor, MiniMax-M2. Benchmarks show that it rivals or approaches top proprietary models while remaining fully open for local deployment and customization. -
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Stable Diffusion Web UI Extensions
Extension index for stable-diffusion-webui
This repository serves as the official index used by the Stable Diffusion Web UI to discover and install extensions. It aggregates metadata for hundreds of community plugins—image utilities, ControlNet tools, upscalers, prompt helpers, animation suites—so users can browse and add capabilities directly from the UI. The index maintains short descriptions, tags, and repository links, enabling quick filtering by purpose or workflow. It also standardizes submission format so extension authors can... -
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CO3D (Common Objects in 3D)
Tooling for the Common Objects In 3D dataset
CO3Dv2 (Common Objects in 3D, version 2) is a large-scale 3D computer vision dataset and toolkit from Facebook Research designed for training and evaluating category-level 3D reconstruction methods using real-world data. It builds upon the original CO3Dv1 dataset, expanding both scale and quality—featuring 2× more sequences and 4× more frames, with improved image fidelity, more accurate segmentation masks, and enhanced annotations for object-centric 3D reconstruction. CO3Dv2 enables research... -
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UCO3D
Uncommon Objects in 3D dataset
uCO3D is a large-scale 3D vision dataset and toolkit centered on turn-table videos of everyday objects drawn from the LVIS taxonomy. It provides about 170,000 full videos per object instance rather than still frames, along with per-video annotations including object masks, calibrated camera poses, and multiple flavors of point clouds. Each sequence also ships with a precomputed 3D Gaussian Splat reconstruction, enabling fast, differentiable rendering workflows and modern implicit/point-based... -
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FlashMLA
FlashMLA: Efficient Multi-head Latent Attention Kernels
FlashMLA is a high-performance decoding kernel library designed especially for Multi-Head Latent Attention (MLA) workloads, targeting NVIDIA Hopper GPU architectures. It provides optimized kernels for MLA decoding, including support for variable-length sequences, helping reduce latency and increase throughput in model inference systems using that attention style. The library supports both BF16 and FP16 data types, and includes a paged KV cache implementation with a block size of 64 to... -
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IQuest-Coder-V1 Model Family
New family of code large language models (LLMs)
IQuest-Coder-V1 is a cutting-edge family of open-source large language models specifically engineered for code generation, deep code understanding, and autonomous software engineering tasks. These models range from tens of billions to smaller footprints and are trained on a novel code-flow multi-stage paradigm that captures how real software evolves over time — not just static code snapshots — giving them a deeper semantic understanding of programming logic. They support native long contexts up to 128K tokens, enabling them to reason across large codebases and multi-file interactions without context fragmentation, and include “Thinking” variants optimized for complex reasoning and “Loop” variants with recurrent mechanisms to improve inference efficiency. ... -
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Granite Code Models
A Family of Open Foundation Models for Code Intelligence
...IBM’s research blog details the motivation for opening these models and points developers to downloads, papers, and hosting options. Together, the materials position Granite Code as enterprise-friendly, permissively licensed models for practical software engineering assistance. They slot into the larger Granite ecosystem that includes language and time-series models, community cookbooks, and production guidance. -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
MiniMax-M1 is presented as the world’s first open-weight, large-scale hybrid-attention reasoning model, designed to push the frontier of long-context, tool-using, and deeply “thinking” language models. It is built on the MiniMax-Text-01 foundation and keeps the same massive parameter budget, but reworks the attention and training setup for better reasoning and test-time compute scaling. Architecturally, it combines Mixture-of-Experts layers with lightning attention, enabling the model to... -
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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... -
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CycleGAN
Software that can generate photos from paintings
CycleGAN — in its original form — is a landmark in deep learning for image-to-image translation without paired data. Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image. This innovation lets the model learn domain-to-domain translations... -
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Leanstral
Open-source code agent designed for Lean 4
Leanstral is an open-weight large language model developed by Mistral AI and specifically designed as a code agent for the Lean 4 proof assistant, enabling advanced interaction with formal mathematics and program verification systems. The model is built to understand and generate Lean 4 code, which is used to express complex mathematical constructs as well as formal software specifications. By focusing on theorem proving and formal reasoning, Leanstral represents a specialized direction within large language models, targeting domains that require strict correctness and logical rigor rather than general conversational tasks. It leverages modern large-scale architectures, likely incorporating mixture-of-experts techniques, to balance efficiency and capability while handling structured symbolic reasoning tasks. ... -
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Mistral Small 4
Model that fuses instruct, reasoning and agentic skills
...The collection reflects an evolution toward hybrid mixture-of-experts architectures that dynamically activate subsets of parameters during inference, allowing large models to remain computationally efficient. Mistral Small 4 models are built to handle tasks such as conversational AI, software development assistance, and reasoning-heavy problem solving, making them versatile tools for both developers and enterprise applications. -
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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% improvement in programming scaffolds. ... -
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Devstral Small 2
Lightweight 24B agentic coding model with vision and long context
Devstral Small 2 is a compact agentic language model designed for software engineering workflows, excelling at tool usage, codebase exploration, and multi-file editing. With 24B parameters and FP8 instruct tuning, it delivers strong instruction following while remaining lightweight enough for local and on-device deployment. The model achieves competitive performance on SWE-bench, validating its effectiveness for real-world coding and automation tasks. -
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Devstral 2
Agentic 123B coding model optimized for large-scale engineering
Devstral 2 is a large-scale agentic language model purpose-built for software engineering tasks, excelling at codebase exploration, multi-file editing, and tool-driven automation. With 123B parameters and FP8 instruct tuning, it delivers strong instruction following for chat-based workflows, coding assistants, and autonomous developer agents. The model demonstrates outstanding performance on SWE-bench, validating its effectiveness in real-world engineering scenarios. -
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Qwen3.6-27B
Dense multimodal Qwen model for coding, agents, and long context
Qwen3.6-27B is an open-weight multimodal model built to deliver strong real-world coding, agent, and long-context performance in a dense 27B-parameter architecture. It combines a causal language model with a vision encoder and supports text, image, and video inputs, making it suitable for both software workflows and broader multimodal tasks. The model emphasizes stability and practical developer utility, with major improvements in agentic coding, frontend generation, and repository-level reasoning. It also introduces thinking preservation, allowing it to retain reasoning traces from earlier turns to improve consistency, reduce repeated computation, and support iterative agent workflows. ... -
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Kimi K2.6
Multimodal agent model for coding, orchestration, and autonomy
Kimi K2.6 is an open-source native multimodal agentic model built for advanced autonomous execution, long-horizon coding, and large-scale task orchestration. It is designed to handle complex end-to-end software workflows across multiple languages and domains, including front-end development, DevOps, performance optimization, and coding-driven design. Beyond coding, it can transform prompts and visual inputs into production-ready interfaces and lightweight full-stack outputs with structured layouts, interactivity, and polished visual detail. ...
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