Search Results for "modes" - Page 5
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Showing 118 open source projects for "modes"
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Procgen
Procedurally-Generated Game-Like Gym-Environments
...The environments are designed to run very quickly (thousands of steps per second on a single core) to facilitate large-scale experiments and make benchmarking efficient. The benchmark supports both “easy” and “hard” difficulty modes, letting researchers trade off computational cost vs challenge. The repo provides a C++ core for game logic and rendering (with support for gym/Gym3 wrappers) plus Python bindings and interactive mode for human play testing. -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario... -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
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Neural Networks Collection
Neural Networks Collection
...So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people. -
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jieba
Stuttering Chinese word segmentation
"Jaba" Chinese word segmentation, do the best Python Chinese word segmentation component. Four word segmentation modes are supported. Precise mode, which tries to cut the sentence most precisely, suitable for text analysis. Full mode, scans all the words that can be formed into words in the sentence, the speed is very fast, but the ambiguity cannot be resolved. The search engine mode, on the basis of the precise mode, divides the long words again to improve the recall rate, which is suitable for word segmentation in search engines. ... -
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Detect and Track
Code release for "Detect to Track and Track to Detect", ICCV 2017
Detect-Track is the official implementation of the ICCV 2017 paper Detect to Track and Track to Detect by Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman. The framework unifies object detection and tracking into a single pipeline, allowing detection to support tracking and tracking to enhance detection performance. Built upon a modified version of R-FCN, the code provides implementations using backbone networks such as ResNet-50, ResNet-101, ResNeXt-101, and Inception-v4, with... -
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CSSgram
CSS library for Instagram filters
Simply put, CSSgram is a library for editing your images with Instagram-like filters directly using CSS. What we're doing is adding filters to the images, as well as applying color and/or gradient overlays via various blending techniques to mimic filter effects. This means less manual image processing and more fun filter effects on the web! We are using pseudo-elements (i.e. :before and :after) to create the filter effects, so you must apply these filters on a containing element (i.e. not a... -
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LARA
Lightweight Architecture for boundedly Rational Agents
...LARA (Lightweight Architecture for boundedly Rational Agents) meets these requirements and fills the gap between frameworks without built-in psychological foundations and full-fledged cognitive architectures which are both not viable options in this context. LARA provides prefabricated components of an agent’s decision process like perception, memory, and different modes of decision making. These components are psychologically plausible, i.e. based on appropriate psychological results and theories. Moreover, interfaces for basic learning and social influence are available. -
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Bavieca (www.bavieca.org)
Bavieca is an open-source speech recognition tookit.
...It comprises the most common acoustic modeling and adaptation techniques including discriminative training, and efficient dynamic and FSM-based decoders that can operate in batch and live recognition modes. Bavieca is entirely written in C++ and distributed under the Apache 2.0 license. Bavieca was developed at Boulder Language Technologies (BLT) during the last three years in response to the needs of the research projects conducted within the company. Research at BLT includes the development of conversational dialog systems and assessment tools that are deployed in formal educational settings and other real-life scenarios. -
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Timeline based editor for creating Compact Disc Subcode Graphics (also known as CD+G or CDG). Both karaoke and multimedia styles of content are supported. Please visit cdgmagic.sf.net for examples playable directly in the HTML5 CD+G player. CD+Graphics Scribe utility (separate download -- click "Browse All Files" above) can now convert existing CDG karaoke content to CMP (CD+Graphics Magic Project), LRC (Enhanced Lyrics), and ASS (Advanced SubStation Alpha) format.
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Online game for developers. Program your team of 4 ants (in LISP) that can reproduce and gain experience and fight against another players in different game modes(CTF, TDM, DM)!
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A utility to extract data from RDBMSs and convert into .arff file format required by WEKA data mining tool set, both interactive wizard and batch working modes.
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Hunyuan-A13B-Instruct
Efficient 13B MoE language model with long context and reasoning modes
...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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DeepSeek-V4-Pro
Flagship MoE model for advanced reasoning, coding, and agents
...DeepSeek-V4-Pro is positioned as the high-end variant of the V4 family, outperforming most open-source models in areas such as agentic coding, STEM reasoning, and world knowledge, and approaching the performance of leading closed-source systems. It also supports advanced reasoning modes and tool-based workflows, enabling autonomous task execution. -
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DeepSeek-V4-Flash
Efficient MoE model for million-token reasoning and coding
...It is trained on more than 32T tokens and refined through a post-training pipeline that includes supervised fine-tuning, reinforcement learning, domain-specific expert cultivation, and on-policy distillation. DeepSeek-V4-Flash supports non-think, think, and think-max reasoning modes, allowing users to balance speed and depth. It is smaller than DeepSeek-V4-Pro but can approach Pro-level reasoning. -
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SuperGemma4
Fast uncensored Gemma model optimized for local chat and coding
...The model is packaged in GGUF format for efficient use with llama.cpp and has been specifically tested on Apple Silicon hardware, delivering high token speeds and smooth local inference. A neutral chat template is embedded to prevent prompt misrouting issues, ensuring consistent responses without unintended shifts into coding or tool-use modes. -
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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. GLM-4.5-Air supports both English and Chinese, and is suitable for tasks involving text generation, coding, reasoning, and tool calling. ... -
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granite-timeseries-ttm-r2
Tiny pre-trained IBM model for multivariate time series forecasting
granite-timeseries-ttm-r2 is part of IBM’s TinyTimeMixers (TTM) series—compact, pre-trained models for multivariate time series forecasting. Unlike massive foundation models, TTM models are designed to be lightweight yet powerful, with only ~805K parameters, enabling high performance even on CPU or single-GPU machines. The r2 version is pre-trained on ~700M samples (r2.1 expands to ~1B), delivering up to 15% better accuracy than the r1 version. TTM supports both zero-shot and fine-tuned...
