Search Results for "active"
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55 projects for "active" with 2 filters applied:
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GLM-4.5
GLM-4.5: Open-source LLM for intelligent agents by Z.ai
GLM-4.5 is a cutting-edge open-source large language model designed by Z.ai for intelligent agent applications. The flagship GLM-4.5 model has 355 billion total parameters with 32 billion active parameters, while the compact GLM-4.5-Air version offers 106 billion total parameters and 12 billion active parameters. Both models unify reasoning, coding, and intelligent agent capabilities, providing two modes: a thinking mode for complex reasoning and tool usage, and a non-thinking mode for immediate responses. They are released under the MIT license, allowing commercial use and secondary development. ... -
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Kimi K2
Kimi K2 is the large language model series developed by Moonshot AI
Kimi K2 is Moonshot AI’s advanced open-source large language model built on a scalable Mixture-of-Experts (MoE) architecture that combines a trillion total parameters with a subset of ~32 billion active parameters to deliver powerful and efficient performance on diverse tasks. It was trained on an enormous corpus of over 15.5 trillion tokens to push frontier capabilities in coding, reasoning, and general agentic tasks while addressing training stability through novel optimizer and architecture design strategies. The model family includes variants like a foundational base model that researchers can fine-tune for specific use cases and an instruct-optimized variant primed for general-purpose chat and agent-style interactions, offering flexibility for both experimentation and deployment. ... -
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TurboFieldfare
Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook
...Instead of loading the entire model, it keeps the shared core and KV cache in RAM while streaming only the routed experts required for each token from SSD. This approach reduces active memory use to roughly 2 GB while the installed model occupies about 14.3 GB of storage. The project includes a native Mac application, command-line tools, a streaming installer, a Swift library, and an experimental OpenAI-compatible local server. Quantized weights, custom Metal kernels, chunked prefill, and a bounded expert cache improve efficiency. ... -
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DeepSeek R1
Open-source, high-performance AI model with advanced reasoning
...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 integrates large-scale reinforcement learning (RL) without relying on supervised fine-tuning, enabling the model to develop advanced reasoning capabilities. This approach has resulted in performance comparable to leading models like OpenAI's o1, while maintaining cost-efficiency. ... -
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llama.cpp
Port of Facebook's LLaMA model in C/C++
The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments. -
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Surya
Implementation of the Surya Foundation Model for Heliophysics
...Named after the Sanskrit word for “sun,” Surya is trained on nine years of high‑resolution solar imagery from NASA’s Solar Dynamics Observatory (SDO). It is designed to forecast solar phenomena—such as flares, solar wind, irradiance, and active region behavior—by predicting future solar images with a sophisticated long–short vision transformer architecture, thereby enabling improved space weather forecasting. Foresees solar flares, wind, EUV spectra, and active region formation in advance. Achieves approximately 16% improvement in forecasting accuracy over traditional methods. 366-million‑parameter foundation model capturing general-purpose solar representations. -
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Qwen3.6
Qwen3.6 is the large language model series developed by Qwen team
The Qwen3.6 project is an open-source large language model series developed by Alibaba’s Qwen team, designed to deliver high-performance AI capabilities with a strong emphasis on real-world usability and developer productivity. It builds upon the advancements introduced in Qwen3.5, focusing on improving stability, responsiveness, and practical application in coding and agent-based workflows. The repository serves as a central hub for documentation, community discussion, and access to the... -
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HunyuanImage-3.0
A Powerful Native Multimodal Model for Image Generation
HunyuanImage-3.0 is a powerful, native multimodal text-to-image generation model released by Tencent’s Hunyuan team. It unifies multimodal understanding and generation in a single autoregressive framework, combining text and image modalities seamlessly rather than relying on separate image-only diffusion components. It uses a Mixture-of-Experts (MoE) architecture with many expert subnetworks to scale efficiently, deploying only a subset of experts per token, which allows large parameter... -
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MiniMax-01
Large-language-model & vision-language-model based on Linear Attention
MiniMax-01 is the official repository for two flagship models: MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model built on top of it. MiniMax-Text-01 uses a hybrid attention architecture that blends Lightning Attention, standard softmax attention, and Mixture-of-Experts (MoE) routing to achieve both high throughput and long-context reasoning. It has 456 billion total parameters with 45.9 billion activated per token and is trained with advanced parallel... -
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4M
4M: Massively Multimodal Masked Modeling
...The repository releases code and models for multiple variants (e.g., 4M-7 and 4M-21), emphasizing transfer to unseen tasks and modalities. Training/inference configs and issues discuss things like depth tokenizers, input masks for generation, and CUDA build questions, signaling active research iteration. The design leans into flexibility and steerability, so prompts and masks can shape behavior without bespoke heads per task. In short, 4M provides a unified recipe to pretrain large multimodal models that generalize broadly while remaining practical to fine-tune. -
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MiniMax-M2
MiniMax-M2, a model built for Max coding & agentic workflows
MiniMax-M2 is an open-weight large language model designed specifically for high-end coding and agentic workflows while staying compact and efficient. It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real... -
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GPT-2 Output Dataset
Dataset of GPT-2 outputs for research in detection, biases, and more
...The repository provides scripts and metadata for working with the dataset, with the goal of supporting research in areas like detection, evaluation of text coherence, and analysis of generative models. While no active development is expected, the dataset remains a useful benchmark for tasks involving text classification, style analysis, and generative model evaluation. -
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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. Its... -
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Mistral Large 4
1T multimodal MoE model for reasoning, coding, and autonomous agents
Mistral Large 4 1T-A52B is Mistral AI's flagship open-weight multimodal Mixture-of-Experts model, designed for advanced reasoning, coding, visual understanding, and autonomous agents. It contains approximately one trillion total parameters, with 49B active per token in its backbone and 52B including embeddings and output layers. Its architecture uses 128 routed experts, four active per token, and one shared expert. The model supports a one-million-token context window and accepts text and images while generating text. It offers configurable low, medium, and high reasoning effort, native function calling, and strict JSON schema enforcement. ... -
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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. ... -
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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. ZAYA1-8B is optimized for long-form reasoning and test-time compute workflows, making it particularly effective for mathematical problem solving, coding tasks, and advanced reasoning chains. ... -
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Kolibri-1
Efficient bilingual MoE model for reasoning, coding, RAG, and agents
...It contains 78B total parameters while activating only 3.46B per token, providing substantial model capacity with relatively low inference compute. Its 50-layer architecture uses 384 experts per layer, with six routed experts and one shared expert active during processing, alongside a 4:1 combination of sliding-window and grouped-query attention. Kolibri supports explicit reasoning with configurable low, medium, and high effort levels, as well as structured tool calling for agentic workflows. Its native 262K-token context can scale to a validated 1M tokens, enabling long-document processing, RAG, research, and extended coding tasks. ... -
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gpt-oss-20b
OpenAI’s compact 20B open model for fast, agentic, and local use
GPT-OSS-20B is OpenAI’s smaller, open-weight language model optimized for low-latency, agentic tasks, and local deployment. With 21B total parameters and 3.6B active parameters (MoE), it fits within 16GB of memory thanks to native MXFP4 quantization. Designed for high-performance reasoning, it supports Harmony response format, function calling, web browsing, and code execution. Like its larger sibling (gpt-oss-120b), it offers adjustable reasoning depth and full chain-of-thought visibility for better interpretability. ... -
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gpt-oss-120b
OpenAI’s open-weight 120B model optimized for reasoning and tooling
GPT-OSS-120B is a powerful open-weight language model by OpenAI, optimized for high-level reasoning, tool use, and agentic tasks. With 117B total parameters and 5.1B active parameters, it’s designed to fit on a single H100 GPU using native MXFP4 quantization. The model supports fine-tuning, chain-of-thought reasoning, and structured outputs, making it ideal for complex workflows. It operates in OpenAI’s Harmony response format and can be deployed via Transformers, vLLM, Ollama, LM Studio, and PyTorch. ... -
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GLM-4.5-Air
Compact hybrid reasoning language model for intelligent responses
GLM-4.5-Air is a multilingual large language model with 106 billion total parameters and 12 billion active parameters, designed for conversational AI and intelligent agents. It is part of the GLM-4.5 family developed by Zhipu AI, offering hybrid reasoning capabilities via two modes: a thinking mode for complex reasoning and tool use, and a non-thinking mode for immediate responses. The model is optimized for efficiency and deployment, delivering strong results across 12 industry benchmarks, with a composite score of 59.8. ... -
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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. It also uses Manifold-Constrained Hyper-Connections (mHC) to improve scaling efficiency and was pretrained on a 30-trillion-token multimodal corpus. ... -
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Solar Open 2
Efficient 250B MoE model for agents, coding, and long-context work
Solar Open 2 is Upstage’s 250B-A15B open-weight large language model designed for agentic workflows, office productivity, document-intensive tasks, coding, and reasoning. Its Hybrid-Attention Mixture-of-Experts architecture contains 250B total parameters while activating only 15B per token, combining three linear-attention layers with one softmax-attention layer for efficient inference. The model supports a native 1M-token context window and uses NoPE instead of rotary positional encoding,... -
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Nemotron 3.5 Lightning
Efficient 30B MoE model for long-running agents and local inference
NVIDIA Nemotron 3.5 Lightning 30B-A3B NVFP4 is an open large language model optimized for efficient autonomous agents, sub-agent deployments, and local inference. It uses a hybrid Mixture-of-Experts architecture combining Mamba-2, MoE, and selected attention layers, with 30B total parameters but only 3B active during inference. The model supports context windows up to 1 million tokens, enabling long-running workflows and large-context reasoning. Its NVFP4 quantization reduces deployment requirements while targeting NVIDIA hardware ranging from DGX Spark and RTX 5090 systems to H100, H200, and GB200 accelerators. NVIDIA also provides DSpark, Multi-Token Prediction, and DFlash speculative decoding methods to accelerate text generation. ... -
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Inkling-Small
Efficient multimodal MoE model for coding, tools, and reasoning
...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. Its 42-layer decoder routes each token through six of 256 specialized experts plus two shared experts, while hybrid local and global attention supports efficient processing. Inkling-Small performs strongly across software engineering, tool use, mathematics, vision, and audio benchmarks, including 80.2% on SWE-Bench Verified and 95.5% on AIME 2026. -
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Inkling
Frontier multimodal MoE model for coding and AI agent workflows
Inkling is Thinking Machines Lab’s first open-weight flagship multimodal Mixture-of-Experts model, designed for advanced reasoning, coding, and autonomous agent workflows. It contains 975B total parameters with 41B active parameters per token, balancing frontier-level capability with efficient sparse inference. The model natively processes text, images, audio, and video within a unified architecture and supports an exceptionally large 1 million token context window for long-document reasoning, repository-scale coding, and agentic execution. Trained from scratch on approximately 45 trillion multimodal tokens, Inkling introduces controllable reasoning effort, allowing users to trade off latency and reasoning depth depending on the task. ...
