Search Results for "nano"
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Showing 11 open source projects for "nano"
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NeuTTS Nano
On-device TTS model by Neuphonic
NeuTTS Nano is an open-source collection of on-device text-to-speech speech language models from Neuphonic. It is built for natural-sounding voice generation that can run locally instead of relying on a remote web API. The project emphasizes instant voice cloning, real-time performance, and deployment on smaller devices such as phones, laptops, and Raspberry Pi-class hardware. -
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nano-graphrag
A simple, easy-to-hack GraphRAG implementation
nano-graphrag is a lightweight implementation of the GraphRAG approach designed to simplify experimentation with graph-based retrieval-augmented generation systems. GraphRAG expands traditional RAG pipelines by constructing knowledge graphs from documents and using relationships between entities to improve the quality and reasoning of AI responses. -
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Nano-vLLM
A lightweight vLLM implementation built from scratch
...Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. The engine is intended primarily for educational use, experimentation, and lightweight deployments where a full production-grade inference stack may be unnecessary. -
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MOSS-TTS-Nano
MOSS-TTS-Nano is an open-source multilingual tiny speech generation
MOSS-TTS-Nano is a lightweight text-to-speech model designed for real-time voice generation in resource-constrained environments. It is part of the broader MOSS-TTS family and focuses on delivering high-quality speech synthesis with a compact architecture. The model operates efficiently on CPU-only systems, enabling deployment without specialized hardware. -
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Banana Slides
A native AI PPT generation application based on nano banana pro
Banana Slides is an open-source application designed to automatically generate presentation slides using artificial intelligence. Built on top of the Nano Banana Pro framework, the software enables users to transform simple prompts or outlines into complete slide decks without manually formatting content. Instead of relying on traditional slide editing workflows, the system allows users to describe the desired presentation in natural language and have the AI generate structured slides, including titles, bullet points, and layout suggestions. ... -
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Porcupine
On-device wake word detection powered by deep learning
...It is using deep neural networks trained in real-world environments. Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64). Scalable. It can detect multiple always-listening voice commands with no added runtime footprint. Self-service. Developers can train custom wake word models using Picovoice Console. Porcupine is the right product if you need to detect one or a few static (always-listening) voice commands. ... -
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Navigation Shortest Map Dhaval Kadia
Shortest Path Navigator with Directions and Distance
Programmed on Language C. A nano-Infant of GOOGLE-MAP Optimized to Consume some MB instead of 50 GB of RAM This was Programmed Quarter Year ago in 2014 -
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Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is a mid-sized open large language model created by NVIDIA to provide strong reasoning and conversational capabilities while maintaining efficient deployment requirements. The model contains roughly 30 billion parameters and is designed to balance performance and computational efficiency, making it suitable for developers building AI applications that cannot run extremely large models. -
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Nemotron 3
Large language model developed and released by NVIDIA
NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 is a state-of-the-art large language model developed and released by NVIDIA as part of its Nemotron 3 family, optimized for high-efficiency inference and strong reasoning performance in open AI workloads. It is the post-trained and FP8-quantized variant of the Nemotron 3 Nano model, meaning its weights and activations are represented in 8-bit floating point (FP8) to dramatically reduce memory usage and computational cost while retaining high accuracy. ... -
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Jan-v1-edge
Jan-v1-edge: efficient 1.7B reasoning model optimized for edge devices
...The model was refined through a two-stage post-training process: Supervised Fine-Tuning (SFT) to transfer knowledge from Jan-v1, followed by Reinforcement Learning with Verifiable Rewards (RLVR) to optimize reasoning, tool use, and correctness. With just 1.7B parameters, Jan-v1-edge achieves 83% accuracy on SimpleQA tasks, approaching the performance of larger models like Jan-nano-128k. Benchmark comparisons show it remains competitive or superior in areas such as EQBench and recency QA, though with slight trade-offs in instruction following and creative writing compared to similar-sized Qwen models.
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