Open Source BSD Artificial Intelligence Software - Page 2
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Artificial Intelligence Software for BSD
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GLM-4.7
Advanced language and coding AI model
GLM-4.7 is an advanced agent-oriented large language model designed as a high-performance coding and reasoning partner. It delivers significant gains over GLM-4.6 in multilingual agentic coding, terminal-based workflows, and real-world developer benchmarks such as SWE-bench and Terminal Bench 2.0. The model introduces stronger “thinking before acting” behavior, improving stability and accuracy in complex agent frameworks like Claude Code, Cline, and Roo Code. GLM-4.7 also advances “vibe coding,” producing cleaner, more modern UIs, better-structured webpages, and visually improved slide layouts. Its tool-use capabilities are substantially enhanced, with notable improvements in browsing, search, and tool-integrated reasoning tasks. Overall, GLM-4.7 shows broad performance upgrades across coding, reasoning, chat, creative writing, and role-play scenarios. -
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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. GLM-4.5 achieves strong performance on 12 industry-standard benchmarks, ranking 3rd overall, while GLM-4.5-Air balances competitive results with greater efficiency. The models support FP8 and BF16 precision, and can handle very large context windows of up to 128K tokens. Flexible inference is supported through frameworks like vLLM and SGLang with tool-call and reasoning parsers included. -
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CLISP is a portable ANSI Common Lisp implementation and development environment by Bruno Haible. Interpreter, compiler, debugger, CLOS, MOP, FFI, Unicode, sockets, CLX. UI in English, German, French, Spanish, Dutch, Russian, and Danish.
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DeepSeek R1
Open-source, high-performance AI model with advanced reasoning
DeepSeek-R1 is an open-source large language model developed by DeepSeek, designed to excel in complex reasoning tasks across domains such as mathematics, coding, and language. 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. To further support the research community, DeepSeek has released distilled versions of the model based on architectures such as LLaMA and Qwen. -
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GLM-4.6
Agentic, Reasoning, and Coding (ARC) foundation models
GLM-4.6 is the latest iteration of Zhipu AI’s foundation model, delivering significant advancements over GLM-4.5. It introduces an extended 200K token context window, enabling more sophisticated long-context reasoning and agentic workflows. The model achieves superior coding performance, excelling in benchmarks and practical coding assistants such as Claude Code, Cline, Roo Code, and Kilo Code. Its reasoning capabilities have been strengthened, including improved tool usage during inference and more effective integration within agent frameworks. GLM-4.6 also enhances writing quality, producing outputs that better align with human preferences and role-playing scenarios. Benchmark evaluations demonstrate that it not only outperforms GLM-4.5 but also rivals leading global models such as DeepSeek-V3.1-Terminus and Claude Sonnet 4. -
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COLMAP
Structure-from-Motion and Multi-View Stereo
COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for the reconstruction of ordered and unordered image collections. The software is licensed under the new BSD license. -
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Hands-On Large Language Models
Official code repo for the O'Reilly Book
Hands-On-Large-Language-Models is the official GitHub code repository accompanying the practical technical book Hands-On Large Language Models authored by Jay Alammar and Maarten Grootendorst, providing a comprehensive collection of example notebooks, code labs, and supporting materials that illustrate the core concepts and real-world applications of large language models. The repository is structured into chapters that align with the educational progression of the book — covering everything from foundational topics like tokens, embeddings, and transformer architecture to advanced techniques such as prompt engineering, semantic search, retrieval-augmented generation (RAG), multimodal LLMs, and fine-tuning. Each chapter contains executable Jupyter notebooks that are designed to be run in environments like Google Colab, making it easy for learners to experiment interactively with models, visualize attention patterns, implement classification and generation tasks. -
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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. With its high-dimensional attention mechanisms and expert routing, Kimi-K2 excels across benchmarks in live coding, math reasoning, and problem solving. -
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NeuralNote
Audio Plugin for Audio to MIDI transcription using deep learning
NeuralNote is an open-source audio software tool designed to convert recorded audio into MIDI data using modern machine learning techniques. The software functions as an audio plugin that can be used inside digital audio workstations as well as a standalone application for music production and analysis. Its main purpose is to perform audio-to-MIDI transcription, allowing musicians to record a performance and automatically transform it into editable MIDI notes. NeuralNote supports polyphonic transcription, meaning it can detect multiple notes played simultaneously, making it useful for instruments such as piano or guitar. The system relies on neural network models to analyze audio signals and infer pitch, timing, and other musical attributes that can be represented as MIDI data. The resulting MIDI output can be edited, quantized, or exported to other instruments within a music production workflow. -
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LTX-2
Python inference and LoRA trainer package for the LTX-2 audio–video
LTX-2 is a powerful, open-source toolkit developed by Lightricks that provides a modular, high-performance base for building real-time graphics and visual effects applications. It is architected to give developers low-level control over rendering pipelines, GPU resource management, shader orchestration, and cross-platform abstractions so they can craft visually compelling experiences without starting from scratch. Beyond basic rendering scaffolding, LTX-2 includes optimized math libraries, resource loaders, utilities for texture and buffer handling, and integration points for native event loops and input systems. The framework targets both interactive graphical applications and media-rich experiences, making it a solid foundation for games, creative tools, or visualization systems that demand both performance and flexibility. While being low-level, it also provides sensible defaults and helper abstractions that reduce boilerplate and help teams maintain clear, maintainable code. -
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Z-Image
Image generation model with single-stream diffusion transformer
Z-Image is an efficient, open-source image generation foundation model built to make high-quality image synthesis more accessible. With just 6 billion parameters — far fewer than many large-scale models — it uses a novel “single-stream diffusion Transformer” architecture to deliver photorealistic image generation, demonstrating that excellence does not always require extremely large model sizes. The project includes several variants: Z-Image-Turbo, a distilled version optimized for speed and low resource consumption; Z-Image-Base, the full-capacity foundation model; and Z-Image-Edit, fine-tuned for image editing tasks. Despite its compact size, Z-Image produces outputs that closely rival those from much larger models — including strong rendering of bilingual (English and Chinese) text inside images, accurate prompt adherence, and good layout and composition. -
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Hunspell is a spell checker and morphological analyzer library and program designed for languages with rich morphology and complex compounding or character encoding. Hunspell interfaces: Curses, Ispell compatible pipe interface, OpenOffice.org UNO module
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Frigate
NVR with realtime local object detection for IP cameras
Frigate - NVR With Realtime Object Detection for IP Cameras A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead. -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short phrase or exemplars, scaling to a vastly larger set of categories than traditional closed-set models. This capability is grounded in a new data engine that automatically annotated over four million unique concepts, producing a massive open-vocabulary segmentation dataset and enabling the model to achieve 75–80% of human performance on the SA-CO benchmark, which itself spans 270K unique concepts. -
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WanGP
AI video generator optimized for low VRAM and older GPUs use
Wan2GP is an open source AI video generation toolkit designed to make modern generative models accessible on consumer-grade hardware with limited GPU memory. It acts as a unified interface for running multiple video, image, and audio generation models, including Wan-based models as well as other systems like Hunyuan Video, Flux, and Qwen. A key focus of the project is reducing VRAM requirements, enabling some workflows to run on as little as 6 GB while still supporting older Nvidia and certain AMD GPUs. Wan2GP provides a full web-based interface that simplifies interaction with complex generative pipelines, making it easier to configure prompts, models, and rendering settings. It also integrates a wide range of utilities such as prompt enhancement, mask editing, motion design, and extraction tools for pose, depth, and flow data to support advanced video workflows. -
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VoxCPM
TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning
VoxCPM is a tokenizer-free text-to-speech system that models speech in a continuous space, aiming for extremely realistic, context-aware synthesis and true-to-life zero-shot voice cloning. Instead of converting speech into discrete tokens, it uses an end-to-end diffusion-autoregressive architecture built on the MiniCPM-4 backbone, combining hierarchical language modeling, finite scalar quantization (FSQ), and local Diffusion Transformers. This design helps decouple semantic and acoustic information while preserving fine-grained prosody, leading to more stable and expressive generation than many discrete-token systems. Trained on a large 1.8-million-hour bilingual corpus, VoxCPM can infer appropriate speaking style from context, dynamically adjusting intonation, rhythm, and emotional tone. It supports zero-shot voice cloning from a short reference audio clip, capturing timbre, accent, and pacing to closely mimic a target speaker without per-speaker fine-tuning. -
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FLUX.2
Official inference repo for FLUX.2 models
FLUX.2 is a state-of-the-art open-weight image generation and editing model released by Black Forest Labs aimed at bridging the gap between research-grade capabilities and production-ready workflows. The model offers both text-to-image generation and powerful image editing, including editing of multiple reference images, with fidelity, consistency, and realism that push the limits of what open-source generative models have achieved. It supports high-resolution output (up to ~4 megapixels), which allows for photography-quality images, detailed product shots, infographics or UI mockups rather than just low-resolution drafts. FLUX.2 is built with a modern architecture (a flow-matching transformer + a revamped VAE + a strong vision-language encoder), enabling strong prompt adherence, correct rendering of text/typography in images, reliable lighting, layout, and physical realism, and consistent style/character/product identity across multiple generations or edits. -
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LLPlayer
The media player for language learning, with dual subtitles
LLPlayer is an open-source media player designed specifically for language learning through video content. Unlike traditional media players, the application focuses on advanced subtitle-related features that help learners understand and interact with foreign language media more effectively. The player supports dual subtitles so users can simultaneously view text in both the original language and their native language while watching videos. It can also automatically generate subtitles in real time using speech-to-text systems such as Whisper, allowing subtitles to be created even when none are available. Real-time translation capabilities enable subtitles to be translated using multiple translation engines and language models. Additional tools such as instant word lookup, contextual translation, and subtitle search allow learners to interact with the text while watching videos. -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. By presenting clear performance estimates and compatibility guidance, the project reduces the trial-and-error typically involved in local LLM experimentation. Overall, llmfit serves as a practical decision assistant for developers who want to run language models efficiently on their own machines. -
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OpenManus
Open-source AI agent framework
OpenManus is an open-source AI agent framework designed to autonomously execute complex, multi-step tasks by combining reasoning, planning, and tool use. It enables developers to build agents that can think, act, and iterate toward goals rather than simply responding to prompts. The platform emphasizes task decomposition, allowing agents to break down objectives into smaller steps and execute them sequentially or recursively. OpenManus supports integration with external tools, APIs, and environments, making it suitable for real-world automation workflows. It is built to be flexible and extensible, enabling customization of agent behaviors, tools, and reasoning strategies. Overall, OpenManus provides a foundation for creating more capable, autonomous AI systems that can handle dynamic and goal-driven tasks. -
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OBLITERATUS
OBLITERATE THE CHAINS THAT BIND YOU
OBLITERATUS is an advanced open-source toolkit designed to analyze and modify the internal behavior of large language models by identifying and removing mechanisms responsible for refusal or restricted responses. It implements a set of techniques collectively referred to as “abliteration,” which target specific internal representations within neural networks to alter how models respond to certain prompts. Unlike traditional fine-tuning approaches, OBLITERATUS operates directly on model activations, enabling behavioral changes without retraining the model. The toolkit provides a full pipeline for probing, analyzing, and modifying model behavior, including visualization tools that help researchers understand where and how refusal mechanisms are encoded. It supports multiple analytical methods such as PCA and SVD to locate these behavioral directions within model layers. -
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eGuideDog project develops free software for the blind. Currently, we focus on WebSpeech, Ekho TTS and WebAnywhere.
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pdfsandwich generates "sandwich" OCR pdf files, i.e. pdf files which contain only images (but no editable text) will be processed by optical character recognition (OCR) and the text will be added to each page invisibly "behind" the images. pdfsandwich is a command line tool which is supposed to be useful to OCR scanned books or journals. It is able to recognize the page layout even for multicolumn text. Essentially, pdfsandwich is a wrapper script which calls the following binaries: convert, unpaper, tesseract, gs, and hocr2pdf (if tesseract < 3.03). It is known to run on Unix systems and has been tested on Linux and MacOS X. It supports parallel processing on multiprocessor systems. In contrast to most competing sandwich programs, it performs preprocessing of the scanned images, such as de-skewing or removal of dark edges etc. For further information please read the manual: http://www.tobias-elze.de/pdfsandwich/index.html
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DiffSinger
Singing Voice Synthesis via Shallow Diffusion Mechanism
DiffSinger is an open-source PyTorch implementation of a diffusion-based acoustic model for singing-voice synthesis (SVS) and also text-to-speech (TTS) in a related variant. The core idea is to view generation of a sung voice (mel-spectrogram) as a diffusion process: starting from noise, the model iteratively “denoises” while being conditioned on a music score (lyrics, pitch, musical timing). This avoids some of the typical problems of prior SVS models — like over-smoothing or unstable GAN training — and produces more realistic, expressive, and natural-sounding singing. The method introduces a “shallow diffusion” mechanism: instead of diffusing over many steps, generation begins at a shallow step determined adaptively, which leverages prior knowledge learned by a simple mel-spectrogram decoder and speeds up inference. -
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Clippy
Clippy, now with some AI
Clippy is an open-source desktop assistant that allows users to run modern large language models locally while presenting them through a nostalgic interface inspired by Microsoft’s classic Clippy assistant from the 1990s. The project serves as both a playful homage to the early days of personal computing and a practical demonstration of local AI inference. Clippy integrates with the llama.cpp runtime to run models directly on a user’s computer without requiring cloud-based AI services. It supports models in the GGUF format, which allows it to run many publicly available open-source LLMs efficiently on consumer hardware. Users interact with the system through a simple animated assistant interface that can answer questions, generate text, and perform conversational tasks. The application includes one-click installation support for several popular models such as Meta’s Llama, Google’s Gemma, and other open models.
