Search Results for "bayesian mixture model" - Page 6
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Showing 171 open source projects for "bayesian mixture model"
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GMM-GMR is a light package of functions in C/C++ to compute Gaussian Mixture Model (GMM) and Gaussian Mixture Regression (GMR). It allows to encode any dataset in a GMM, and GMR can then be used to retrieve partial data by specifying the desired inputs.
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Matlab Classification Toolbox contains implementations of the following classifiers: Naive Bayes, Gaussian, Gaussian Mixture Model, Decision Tree and Neural Networks. This toolbox allows users to compare classifiers across various data sets.
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RISO: distributed belief networks
Distributed, heterogeneous Bayesian belief networks
RISO: distributed, heterogeneous Bayesian belief networks. Belief network: a probability model defined on an acyclic directed graph; distributed: nodes can be on different hosts; and heterogeneous: allowing different types of conditional distributions. -
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A generic XML repository that will store any well-formed XML document in a relational database, using the node labelling scheme of the Nested Sets Model. Interfaces are provided for uploading, DOM manipulation and retrieval. The source code is a mixture o
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A straight-forward Java implementation of a mixture model with pluggable mixture functions, e.g. a mixture of Gaussian functions. The number and dimensionality of the mixture functions is not limited. All critical calculations are performed in log-space.
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Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
...It is trained from scratch and built using a hybrid architecture that integrates Transformer attention layers with Mamba-style sequence modeling components inside a Mixture-of-Experts framework. This architecture allows the system to maintain strong reasoning capabilities while improving throughput and reducing the computational cost associated with large context processing. The model is designed as a general-purpose language system capable of handling tasks such as chat interaction, coding assistance, document analysis, and instruction following. -
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Nemotron 3 Super
Open language model developed by NVIDIA as part of Nemotron-3 family
NVIDIA-Nemotron-3-Super-120B-A12B-FP8 is a large-scale open language model developed by NVIDIA as part of the Nemotron-3 family of generative AI systems designed for advanced reasoning, conversational interaction, and agent-based workflows. The model contains approximately 120 billion parameters, but employs a Mixture-of-Experts architecture that activates only a smaller subset of parameters during inference, improving computational efficiency while maintaining high capability. ... -
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Nemotron 3
Large language model developed and released by NVIDIA
...The base Nano architecture uses a hybrid Mamba-Transformer Mixture-of-Experts (MoE) design, allowing the model to activate only a small fraction of its 31.6 billion parameters per token, which improves speed and efficiency without sacrificing quality on complex queries. This configuration supports a massive context length of up to 1 million tokens, making it suitable for long-context reasoning, agentic tasks, extended dialogues, and applications like code generation or document summarization. -
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Mistral Small 4
Model that fuses instruct, reasoning and agentic skills
The Mistral Small 4 collection is a set of open-weight large language models developed by Mistral AI that aim to unify multiple capabilities, including instruction following, reasoning, and coding, within a single efficient architecture. These models are part of the broader Mistral Small family, which is designed to deliver strong performance across a wide range of everyday AI tasks while maintaining relatively low latency and efficient deployment requirements. The collection reflects an... -
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Kimi K3
Powerful, native multimodal AI agentic model
Kimi K3 is an open-weight, multim is an open-weight, multimodal agentic AI model from Moonshot AI designed for advanced coding, research, reasoning, and knowledge work. Builtodal agentic AI model from Moonshot AI designed for advanced coding, research, reasoning, and knowledge work. Built with 2.8 trillion total parameters and a sparse mixture-of-experts architecture, it activates 104 billion parameters per token to with 2.8 trillion total parameters and a sparse mixture-of-experts architecture, it activates 104 billion parameters per token to deliver frontier-level performance more efficiently. ... -
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Leanstral
Open-source code agent designed for Lean 4
...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. The model can assist in writing proofs, exploring mathematical structures, and validating logical properties in code. -
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Qwen3.6-35B-A3B-FP8
FP8 Qwen model for efficient multimodal coding and agent 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. It is compatible with major inference frameworks such as Transformers, vLLM, SGLang, and KTransformers, making it a practical high-performance option. -
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Hy3 preview
Efficient MoE model for reasoning, coding, and AI agent workflows
Hy3 preview is Tencent Hunyuan’s latest open-weight Mixture-of-Experts language model, designed for advanced reasoning, coding, instruction following, and autonomous agent workflows. It is the first model built on Tencent’s rebuilt training infrastructure and introduces significant improvements in context learning, software engineering, and tool-based task execution. The model features 295B total parameters with only 21B activated during inference, plus a dedicated 3.8B Multi-Token Prediction (MTP) layer that accelerates generation through speculative decoding. ... -
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Qwen3.6-35B-A3B
Open multimodal model for coding, agents, and long-context tasks
...A notable addition is thinking preservation, which allows the model to retain reasoning context from earlier messages, improving iterative work and reducing redundant computation. Architecturally, it uses a Mixture-of-Experts design with 35B total parameters and 3B active, supports a native 262K-token context window, and can be extended to about 1M tokens with YaRN. It also performs strongly across coding, agent, vision, reasoning, and document-understanding benchmarks. -
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dinrhiw2
Dinrhiw2 - machine learning library and tools (neural networks) in C++
Machine learning C++ library. Linear algebra (PCA), splines hermite curve interpolation, neural networks (feedforward, recurrent, RBM, bayesian), Hidden markov model (HMM), Hamiltonian Monte Carlo sampling for neural networks etc. Uses BLAS (OpenBLAS, Intel MKL) to speed up linear algebra. Software is developed by Tomas Ukkonen. -
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ZAYA1-8B
Efficient MoE reasoning model for coding and math workloads
ZAYA1-8B is a compact Mixture-of-Experts reasoning model developed by Zyphra, designed to deliver unusually high intelligence density with fewer than 1 billion active parameters. The model contains 8.4B total parameters with around 760M active during inference, allowing it to achieve strong reasoning, mathematics, and coding performance while remaining lightweight enough for efficient local or on-device deployment. -
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Qwen3.8-2.4T-A95B
Massive 2.4T MoE model for coding, agents, research, and reasoning
Qwen3.8-2.4T-A95B is Qwen’s largest open-weight model and the first Qwen-Max-class model released openly, targeting advanced coding, professional work, research, and long-horizon agentic tasks. It uses a massive Mixture-of-Experts architecture with 2.4 trillion total parameters while activating 95B per token, combining Gated DeltaNet and attention layers across 512 experts. The model emphasizes reliable autonomous execution, including stronger planning, environment feedback handling, and end-to-end completion of complex multi-step workflows. ... -
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Hunyuan-A13B-Instruct
Efficient 13B MoE language model with long context and reasoning modes
Hunyuan-A13B-Instruct is a powerful instruction-tuned large language model developed by Tencent using a fine-grained Mixture-of-Experts (MoE) architecture. While the total model includes 80 billion parameters, only 13 billion are active per forward pass, making it highly efficient while maintaining strong performance across benchmarks. It supports up to 256K context tokens, advanced reasoning (CoT) abilities, and agent-based workflows with tool parsing. -
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Qwable-v1
Agentic coding model combining Opus reasoning and Fable tools
...The result is a 35B Mixture-of-Experts model with only 3B active parameters that can switch between deep reasoning and agent-style execution depending on the system prompt. In normal conversations, it behaves like a reasoning-focused assistant that generates explicit <think> chains before answering. When configured as an agent, it can emit structured tool-use XML for file editing, shell commands, codebase navigation, and workflow automation. -
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Laguna M.1
Flagship Poolside model for agentic coding and software engineering
Laguna M.1 is Poolside’s flagship Mixture-of-Experts model built specifically for agentic coding, software engineering, and long-horizon autonomous workflows. It contains approximately 225.8B total parameters with 23.4B activated per token, making it substantially larger and more capable than Laguna XS.2 while maintaining efficient inference through sparse activation. Trained from scratch on roughly 30 trillion tokens using Poolside’s in-house “Model Factory” pipeline, the model focuses on complex software development tasks, repository-scale reasoning, tool use, and multi-step agent execution. ... -
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Mistral Large 3 675B Instruct 2512 Eagle
Speculative-decoding accelerator for the 675B Mistral Large 3
Mistral Large 3 675B Instruct 2512 Eagle is the dedicated speculative-decoding draft model for the full Mistral Large 3 Instruct system, designed to significantly speed up generation while preserving high output quality. It works alongside the primary 675B instruct model, enabling faster response times by predicting several tokens ahead using Mistral’s Eagle speculative method. Built on the same frontier-scale multimodal Mixture-of-Experts architecture, it complements a system featuring 41B active parameters and a 2.5B-parameter vision encoder. ... -
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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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Laguna XS.2
Open agentic coding model optimized for local deployment
Laguna XS.2 is Poolside’s first open-weight Mixture-of-Experts model designed specifically for agentic coding and long-horizon software engineering tasks. The model contains 33B total parameters with only 3B activated per token, allowing it to deliver strong coding performance while remaining efficient enough to run locally on modern consumer hardware. It uses a hybrid attention architecture that combines Sliding Window Attention and global attention layers, reducing memory requirements and improving inference speed. ... -
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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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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. ...