Search Results for "thinking" - Page 8
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Showing 195 open source projects for "thinking"
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Qwen3.6-27B
Dense multimodal Qwen model for coding, agents, and long context
...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. Qwen3.6-27B natively supports a 262K-token context window and can be extended to about 1M tokens with YaRN for ultra-long tasks. It is compatible with Transformers, vLLM, SGLang, and KTransformers, supports tool calling through Qwen-Agent. -
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Qwen3.6-35B-A3B
Open multimodal model for coding, agents, and long-context tasks
...The model emphasizes stability, responsiveness, and practical developer productivity, with major improvements in agentic coding, frontend generation, and repository-level reasoning. 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. ... -
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Qwen3.8-Flash-Next
Efficient multimodal MoE model for coding, reasoning, and AI agents
...Qwen3.8-Flash-Next natively handles text, images, and video and supports a 262K-token context window extensible to 1M tokens. It targets coding, tool use, professional tasks, computer interaction, multimodal reasoning, and long-horizon agents, with configurable thinking modes and reasoning effort. -
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Qwen3.8-27B
Dense 27B multimodal model for coding, agents, and visual reasoning
...Agent capabilities emphasize autonomous planning, environment feedback, computer and browser use, and reliable completion of complex multi-step workflows. Qwen3.8-27B supports a native 262,144-token context window that can be extended to one million tokens. Thinking is enabled by default, with low, medium, and xhigh reasoning-effort settings and preserved reasoning across conversations. It also delivers substantial improvements over Qwen3.6-27B on coding and agent benchmarks while remaining deployment-friendly and compatible with Transformers, vLLM, SGLang, etc. -
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Qwen3.8-2.4T-A95B
Massive 2.4T MoE model for coding, agents, research, and reasoning
...Qwen3.8 also provides adjustable reasoning depth through low, medium, and xhigh reasoning-effort settings and preserves reasoning context across conversations. It is a text-only, thinking-first model and supports deployment through vLLM, SGLang, and TokenSpeed, with strong results across coding, tool use, research, and professional benchmarks. -
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Ling 3.0 Tiny
Lightweight MoE model for local reasoning, coding, and AI agents
...It contains 7.9B total parameters while activating only 1.3B per token, using a hybrid architecture that alternates Kimi Delta Attention and Multi-Head Latent Attention with a sparse 128-expert MoE. The model supports both fast responses and configurable multi-step thinking, covering general agents, coding, mathematics, scientific reasoning, and instruction following. It is specifically optimized for local and resource-constrained deployment and has been validated on NVIDIA DGX Spark, Apple Silicon MacBooks, and Mac mini systems. FP8 testing reached around 100–105 tokens/s on DGX Spark and 86–90 tokens/s on an M4 Pro MacBook. ... -
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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. ... -
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Gemma 4 12B
Unified multimodal Gemma model for local coding and reasoning
...It supports text, image, audio, and video inputs with text output, making it useful for transcription, image understanding, video analysis, coding, and agentic workflows. The model has 11.95B parameters, 48 layers, a 256K-token context window, and support for over 140 languages. It also includes configurable thinking modes, native system prompt support, function calling, and strong benchmark performance for its size. It is optimized for consumer GPUs, workstations, and streamlined local deployment. -
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Gemma 4
Google’s flagship dense multimodal model for coding and reasoning
...The model contains approximately 30.7B parameters and supports text and image inputs with text generation output, while also processing video as image-frame sequences. Built as the most capable model in the Gemma 4 family, it combines strong reasoning performance with a large 256K-token context window and configurable thinking modes. Gemma 4 31B supports native function calling, structured outputs, and more than 140 languages, making it suitable for enterprise assistants, coding agents, document analysis, and multilingual applications. Google positions it as a frontier-level model that can run on consumer GPUs and workstations while achieving leading results across reasoning, mathematics, coding, and multimodal benchmarks. -
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Qwen3.6-35B-A3B-FP8
FP8 Qwen model for efficient multimodal coding and agent tasks
...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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DeepSeek-V3.2
High-efficiency reasoning and agentic intelligence model
...DeepSeek-V3.2 also features a large-scale agentic task synthesis pipeline, which generates training data to enhance tool-use intelligence and multi-step reasoning. It introduces a new “thinking with tools” chat template, allowing it to reason and decide when to invoke specific tools during problem solving. -
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EORS
rational agent
This project aims at creating rationally thinking agents. The agent gather information through command line or network and stores it in its memory. It uses Stanford's NLP library to understand the language statements. -
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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... -
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Hy4 preview
770B MoE model for coding, research, reasoning, and long-context work
Hy4 preview is Tencent’s open-weight flagship Mixture-of-Experts language model designed for advanced reasoning, software engineering, productivity, scientific research, and long-horizon tasks. It contains 770B backbone parameters while activating 49B per token across 78 layers, with 256 routed experts and one shared expert in each MoE layer. Its architecture uses Gated DeepSeek Sparse Attention with IndexCache for cross-layer sparse-index reuse and identity Hyper-Connections to improve... -
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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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DiffusionGemma
NVFP4 DiffusionGemma model for fast multimodal text generation
...Its diffusion-based generation produces tokens in parallel 256-token blocks, enabling very high-speed output, with reported generation above 1,100 tokens per second on NVIDIA Hopper H100 in FP8. The model supports a 256K-token context window, configurable thinking mode, native function calling, structured JSON output, and multilingual inference across 35+ languages. The NVFP4 quantization reduces weights and activations from 16-bit to 4-bit, lowering disk size and GPU memory needs for vLLM deployment. -
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Laguna XS.2
Open agentic coding model optimized for local deployment
...It uses a hybrid attention architecture that combines Sliding Window Attention and global attention layers, reducing memory requirements and improving inference speed. Laguna XS.2 supports native reasoning with interleaved thinking between tool calls, enabling more capable autonomous coding agents and multi-step workflows. The model features a 262K-token context window, preserved reasoning across interactions, FP8 KV-cache optimization, and compatibility with local deployment ecosystems such as Ollama and vLLM. -
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Hunyuan-A13B-Instruct
Efficient 13B MoE language model with long context and reasoning modes
...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. The model offers both fast and slow thinking modes, letting users trade off speed for deeper reasoning. It excels in mathematics, science, coding, and multi-turn conversation tasks, rivaling or outperforming larger models in several areas. Deployment is supported via TensorRT-LLM, vLLM, and SGLang, with Docker images and integration guides provided. Open-source under a custom license, it's ideal for researchers and developers seeking scalable, high-context AI capabilities with optimized inference. -
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Framework for modelling of Natural General Intelligence. This project aims at creation of open source AGI (Artificial General Intelligence) through modelling of natural thinking. See http://roland.pri.ee/bakalaureusetoo/ for theoretical details.
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Ministral 3 3B Reasoning 2512
Compact 3B-param multimodal model for efficient on-device reasoning
...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. The model retains strong system-prompt adherence, supports function-calling with structured JSON output, and offers a large 256k token context window for extended context reasoning.
