Search Results for "flash image software"
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Showing 15 open source projects for "flash image software"
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DSH Web UI
Plugin and skin collection for DeepSeek Harness (DSH) Web UI
DSH Web UI is a modular plugin and skin collection that expands the DeepSeek Harness browser interface into a broader development workspace. Its components attach through the official DSH profile mechanism without modifying the Harness source code. Users can add a task board that executes real agent sessions and supports scheduled jobs through cron expressions. A Git graph visualizes branches and commit history, while additional panels provide files, editing, terminals, Git operations, and... -
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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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GLM-4.6V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.6V represents the latest generation of the GLM-V family and marks a major step forward in multimodal AI by combining advanced vision-language understanding with native “tool-call” capabilities, long-context reasoning, and strong generalization across domains. Unlike many vision-language models that treat images and text separately or require intermediate conversions, GLM-4.6V allows inputs such as images, screenshots or document pages directly as part of its reasoning pipeline — and... -
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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. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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GLM-5.3-Flash
Efficient 320B multimodal MoE model for coding and autonomous agents
...It also uses Manifold-Constrained Hyper-Connections (mHC) to improve scaling efficiency and was pretrained on a 30-trillion-token multimodal corpus. GLM-5.3-Flash supports text and image inputs and is particularly optimized for coding and autonomous agent workloads, approaching larger frontier models on related benchmarks while improving over GLM-5.2. It supports local deployment through SGLang, vLLM, TokenSpeed, and KTransformers and is released under the MIT license. -
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Qwen3.8-Flash-Next
Efficient multimodal MoE model for coding, reasoning, and AI agents
Qwen3.8-Flash-Next is Qwen’s experimental open-weight multimodal model previewing the architecture planned for Qwen4. It uses 125B language-model parameters with only 6B activated per token, supplemented by 51B n-gram embedding parameters and 4B for multi-token prediction. Its hybrid architecture combines Gated DeltaNet with Qwen Sparse Attention (QSA), which processes micro-blocks rather than individual tokens to reduce latency in long-context agent workloads. The model also introduces... -
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MiMo-V2.6-Flash
Efficient 309B omnimodal MoE for coding, agents, vision, and audio
MiMo-V2.6-Flash is Xiaomi MiMo’s efficiency-balanced open-weight omnimodal model, designed to scale reinforcement learning across coding, general agents, visual tasks, and cybersecurity. Its sparse Mixture-of-Experts architecture contains 309B total parameters while activating only 15B per token, using 256 routed experts with eight active per token. The model natively processes text, images, video, and audio and supports a 1M-token context window for large repositories, extended tool traces,... -
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OpenVLA 7B
Vision-language-action model for robot control via images and text
OpenVLA 7B is a multimodal vision-language-action model trained on 970,000 robot manipulation episodes from the Open X-Embodiment dataset. It takes camera images and natural language instructions as input and outputs normalized 7-DoF robot actions, enabling control of multiple robot types across various domains. Built on top of LLaMA-2 and DINOv2/SigLIP visual backbones, it allows both zero-shot inference for known robot setups and parameter-efficient fine-tuning for new domains. The model... -
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Hy3
Open code agent for Lean 4 proofs and formal software verification
...Leanstral accepts text and image inputs and produces text output, enabling multimodal workflows around mathematics, code, and specifications. It supports configurable reasoning effort, allowing users to disable reasoning or enable high-effort reasoning for complex prompts. This updated version of the original Leanstral focuses on performant, cost-effective formal coding and theorem-proving workflows. -
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Leanstral 1.5
Open code agent for Lean 4 proofs and formal software verification
...Leanstral accepts text and image inputs and produces text output, enabling multimodal workflows around mathematics, code, and specifications. It supports configurable reasoning effort, allowing users to disable reasoning or enable high-effort reasoning for complex prompts. This updated version of the original Leanstral focuses on performant, cost-effective formal coding and theorem-proving workflows. -
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Devstral Small 2
Lightweight 24B agentic coding model with vision and long context
...It introduces vision capabilities, enabling image understanding alongside text for more versatile development workflows. Devstral Small 2 supports a 256k context window, allowing it to reason across large repositories, long diffs, and extended technical contexts. Its architecture improves generalization across diverse prompts and coding environments while leveraging advanced attention scaling techniques. -
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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 K2.7 Code
Coding-focused Kimi model for long-horizon agent workflows
...The model supports image and video input, native INT4 quantization, interleaved thinking, and multi-step tool calling. It also forces preserve-thinking mode by default, retaining full reasoning context across multi-turn interactions to improve coding-agent consistency. K2.7 Code is recommended for use through Kimi Code CLI and can be deployed with vLLM, SGLang, or KTransformers. -
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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... -
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MiMo-V2.5
Omnimodal AI model for agents, coding, and long-context tasks
MiMo-V2.5 is a native omnimodal large language model developed by Xiaomi, designed for advanced agentic workflows, multimodal reasoning, and long-context processing. 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...
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