Search Results for "system 1"
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Showing 18 open source projects for "system 1"
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Hunyuan3D-2.1
From Images to High-Fidelity 3D Assets
Hunyuan3D-2.1 is Tencent Hunyuan’s advanced 3D asset generation system that produces high-fidelity 3D models with Physically Based Rendering (PBR) textures. It is fully open-source with released model weights, training, and inference code. It improves on prior versions by using a PBR texture pipeline (enabling realistic material effects like reflections and subsurface scattering) and allowing community fine-tuning and extension. It supports both shape generation (mesh geometry) and texture... -
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Strata
Qwen3.8-Flash-Next on any consumer hardware
Strata is an open-source local inference system for running Qwen3.8-Flash-Next on consumer Windows and Linux PCs. It is designed to make a very large mixture-of-experts model practical with one NVIDIA GPU, system RAM, and local storage instead of server-class hardware. Frequently used experts remain on the GPU while other model components are shared across RAM, CPU, and SSD resources. -
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Kev
Jev-like family of decision models built on top of Qwen3.5/3.8
...Checkpoints range from a compact 0.8B model for smaller hardware to a 27B version for high-end systems. The models include probability calibration and can run through CUDA, ROCm, or MLX depending on hardware. Kev exposes an API compatible with TypeSafe System One, allowing compatible clients to target a locally hosted server. Developers can also fine-tune models on their own labeled examples and deploy private HTTPS endpoints. -
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Strands Decider
Small, fast decision model, or system one model, for agentic workflow
Strands Decider is a compact System One decision model built for fast classification and scoring inside AI agent workflows. Unlike a generative LLM, it chooses among options or rates inputs on an ordered scale without decoding free-form text. Its first model has 1.9 billion parameters and replaces the language-modeling head of Qwen3.5-2B-Base with a small pointer-based decision head. -
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Poetiq
Reproduction of Poetiq's record-breaking submission to the ARC-AGI-1
poetiq-arc-agi-solver is the open-source codebase from Poetiq that replicates their record-breaking submission to the challenging benchmark suite ARC-AGI (both ARC-AGI-1 and ARC-AGI-2). The project demonstrates a system that orchestrates large language models (LLMs) — like those from major providers — with carefully engineered prompting, reasoning workflows, and dynamic strategies, to tackle the abstract, logic-heavy problems in ARC-AGI. Instead of relying on a single prompt or fixed strategy, their solver dynamically adapts the reasoning path, selecting what to ask or analyze next depending on intermediate results — effectively compositing reasoning, perception, and program synthesis (or symbolic manipulation) in a loop. ... -
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VOID
Video Object and Interaction Deletion
...Built on top of transformer-based architectures and fine-tuned for video inpainting tasks, the system uses interaction-aware mask conditioning to ensure temporal consistency across frames. One of its most notable capabilities is its ability to simulate realistic scene behavior after object removal, such as causing an object to fall naturally if its support is removed, which significantly enhances realism. -
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Laya
Non-autoregressive System 1 decision engine
Laya is an open-source, non-autoregressive decision engine designed to answer structured questions without generating free-form text. It supports choice, score, and yes-or-no probability decisions in a single forward pass. The system works across more than 100 languages and uses a router to select an appropriate checkpoint for each request. It can run locally on CPU or GPU hardware and includes Python and TypeScript tooling. Integrations are available for HTTP, MCP, LangChain, LangGraph,... -
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HRM-Text
1B text generation model based on the HRM architecture
HRM-Text is a one-billion-parameter text generation model and pretraining framework based on the Hierarchical Reasoning Model architecture. It is designed to make foundation model pretraining more accessible by reducing compute and data requirements compared with traditional scaling-heavy approaches. The system combines hierarchical recurrent design, task-completion strengthening, and latent-space reasoning. -
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Zola
Open chat interface for all your models
Zola is an open-source chat interface that gives users one place to interact with multiple AI models. It supports providers such as OpenAI, Mistral, Claude, Gemini, OpenRouter, and locally hosted Ollama models. Users can bring their own API keys, upload files, and customize system prompts and layouts. The interface is responsive and includes light and dark themes. Zola can be self-hosted and also supports Docker-based deployment, including setups with Ollama. -
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Kimi-Audio
Audio foundation model excelling in audio understanding
...Instead of fragmenting work across specialized models, Kimi-Audio handles automatic speech recognition (ASR), audio question answering, automatic audio captioning, speech emotion recognition, and audio-to-text chat in one system, enabling developers to build rich, multimodal audio applications without stitching together disparate components. It uses a novel model setup that combines continuous acoustic features with discrete semantic tokens to richly capture sound and meaning across speech, music, and environmental audio. -
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MiniMax-M2.5
State of the art LLM and coding model
MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before... -
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Claude Relay Service
Claude Code image, a one-stop open source transit service
claude-relay-service is an open-source proxy and relay platform that enables unified access to multiple AI model providers through a single self-hosted gateway. The project is designed to help users centralize subscriptions and API usage for services such as Claude, OpenAI, Gemini, and related tools. It acts as a middleware layer that forwards requests while managing authentication, routing, and cost-sharing scenarios. The system is particularly useful for teams or communities that want to... -
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Watermark Anything
Official implementation of Watermark Anything with Localized Messages
Watermark Anything (WAM) is an advanced deep learning framework for embedding and detecting localized watermarks in digital images. Developed by Facebook Research, it provides a robust, flexible system that allows users to insert one or multiple watermarks within selected image regions while maintaining visual quality and recoverability. Unlike traditional watermarking methods that rely on uniform embedding, WAM supports spatially localized watermarks, enabling targeted protection of specific image regions or objects. The model is trained to balance imperceptibility, ensuring minimal visual distortion, with robustness against transformations and edits such as cropping or motion. -
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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... -
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Mistral Medium 3.5
Dense 128B multimodal model for reasoning, coding, vision, and agents
...Reasoning effort can be configured per request, allowing users to switch between fast direct responses and higher-compute reasoning for complex problems and agents. It includes native function calling, structured JSON output, strong system-prompt adherence, and multilingual support across dozens of languages. Mistral positions it as the successor to Medium 3.1, Magistral, and Devstral 2, consolidating their specialized capabilities into one general-purpose model. -
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MiMo-V2.5-Pro
Flagship MoE model for long-context agents and complex coding
...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. Architecturally, it uses a hybrid attention system combining Sliding Window Attention and Global Attention to significantly reduce memory usage while preserving long-context performance. ... -
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MiMo-V2.5
Omnimodal AI model for agents, coding, and long-context tasks
...Built on a Mixture-of-Experts architecture with approximately 309B total parameters and around 15B activated per inference, it balances high capability with efficient execution. The model natively processes text, images, video, and audio within a unified system, enabling cross-modal understanding and complex task execution in a single pipeline. 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. ... -
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Smaug Flash
304B MoE model optimized for agentic coding and long-running workflows
Smaug Flash is an open-weight agentic coding model from Abacus.AI, fine-tuned from DeepSeek-V4-Flash-0731 to improve autonomous software engineering, tool use, and long-running agent workflows. It uses a 304B-parameter Mixture-of-Experts architecture with 43 layers, 256 routed experts, six selected experts per token, and one shared expert. Its attention system combines Multi-Head Latent Attention with a sparse token indexer, while DSpark multi-token prediction provides speculative decoding for faster generation. 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. ...
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