Search Results for "compact framework"
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Showing 20 open source projects for "compact framework"
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model2Vec
Fast State-of-the-Art Static Embeddings
model2vec is an innovative embedding framework that converts large sentence transformer models into compact, high-speed static embedding models while preserving much of their semantic performance. The project focuses on dramatically reducing the computational cost of generating embeddings, achieving significant improvements in speed and model size without requiring large datasets for retraining. -
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ZeroClaw
Fast, small, and fully autonomous AI assistant infrastructure
...The framework features a compact single binary with fast cold and warm startup times and very low memory overhead, making it suitable even for resource-constrained hardware like small servers or edge devices. Security is a first-class concern, with sandbox controls, encrypted secrets, allowlisted operations, and scoped filesystem access by default, helping reduce risk when running autonomous agents. -
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1D Visual Tokenization and Generation
This repo contains the code for 1D tokenizer and generator
...The repo also bundles a full generative modeling pipeline (e.g. the framework “MaskGen” / “TA-TiTok”) that demonstrates how this 1D tokenizer can be used in text-to-image generation or image reconstruction tasks. -
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Solon
Java enterprise application development framework
Solon is a full-scenario Java enterprise application framework that positions itself as a lean, high-performance alternative to heavy stacks. It advertises large concurrency gains, lower memory use, much faster startup, and dramatically smaller packages while remaining compatible from Java 8 through Java 24. The framework focuses on restrained APIs and an open ecosystem, with modules that cover web, data, cloud, and microservice patterns. Its messaging emphasizes “replaceable Spring”... -
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zclaw
Your personal AI assistant at all-in 888KiB
zclaw is a highly compact personal AI assistant framework designed to run on constrained embedded hardware such as the ESP32. The project focuses on delivering core assistant capabilities within an extremely small footprint, demonstrating how AI-driven automation can operate on microcontrollers. It includes support for GPIO control, scheduled tasks, memory handling, and other embedded automation features that enable real-world device interaction. -
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Nano-vLLM
A lightweight vLLM implementation built from scratch
...Its API closely mirrors that of the original vLLM framework, allowing developers familiar with vLLM to adopt the tool with minimal changes. -
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Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
...It breaks down the model into patch embedding, positional encoding, multi-head self-attention, feed-forward blocks, and a classification head so you can understand each component in isolation. The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. ... -
8
llama2.c
Inference Llama 2 in one file of pure C
...The goal of llama2.c is to demonstrate how a compact and transparent implementation can perform meaningful inference even with small models, emphasizing simplicity, clarity, and accessibility. The project builds upon lessons from nanoGPT and takes inspiration from llama.cpp, focusing instead on minimalism and educational value over large-scale performance. -
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litlyx
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Instant Neural Graphics Primitives
Instant neural graphics primitives: lightning fast NeRF and more
...The system implements several neural graphics primitives including neural radiance fields, signed distance functions, neural images, and neural volumes. These representations are trained using a compact neural network combined with a multiresolution hash encoding that dramatically accelerates both training and rendering processes. The framework is capable of reconstructing detailed 3D scenes from images and generating realistic views of those scenes in real time. Compared with earlier neural radiance field approaches, instant-ngp significantly reduces training time and computational requirements, enabling models to be trained within seconds or minutes on modern GPUs. -
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Chat with LLMs Everywhere
Run PyTorch LLMs locally on servers, desktop and mobile
TorchChat is an open-source project from the PyTorch ecosystem designed to demonstrate how large language models can be executed efficiently across different computing environments. The project provides a compact codebase that illustrates how to run conversational AI systems using PyTorch models on laptops, servers, and mobile devices. It is intended primarily as a reference implementation that shows developers how to integrate large language models into applications without requiring a... -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The... -
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minGPT
A minimal PyTorch re-implementation of the OpenAI GPT
minGPT is a minimalist, educational re-implementation of the GPT (Generative Pretrained Transformer) architecture built in PyTorch, designed by Andrej Karpathy to expose the core structure of a transformer-based language model in as few lines of code as possible. It strips away extraneous bells and whistles, aiming to show how a sequence of token indices is fed into a stack of transformer blocks and then decoded into the next token probabilities, with both training and inference supported.... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. ... -
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Old Photo Restoration
Bringing Old Photo Back to Life (CVPR 2020 oral)
We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize. Therefore, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. Specifically, we train two... -
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DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
DeepSDF is a deep learning framework for continuous 3D shape representation using Signed Distance Functions (SDFs), as presented in the CVPR 2019 paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation by Park et al. The framework learns a continuous implicit function that maps 3D coordinates to their corresponding signed distances from object surfaces, allowing compact, high-fidelity shape modeling. -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important... -
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Siamese and triplet learning
Siamese and triplet networks with online triplet mining in PyTorch
Siamese and triplet learning is a PyTorch implementation of Siamese and triplet neural network architectures designed for learning embedding representations in machine learning tasks. These types of networks learn to map images into a compact feature space where the distance between vectors reflects the similarity between inputs. Such embeddings are commonly used in applications like face recognition, image similarity search, and few-shot learning. The repository demonstrates how to train... -
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Netvlad
NetVLAD: CNN architecture for weakly supervised place recognition
NetVLAD is a deep learning-based image descriptor framework developed by Relja Arandjelović for place recognition and image retrieval. It extends standard CNNs with a trainable VLAD (Vector of Locally Aggregated Descriptors) layer to create compact, robust global descriptors from image features. This implementation includes training code and pretrained models using the Pittsburgh and Tokyo datasets. -
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t5-small
T5-Small: Lightweight text-to-text transformer for NLP tasks
T5-Small is a lightweight variant of the Text-To-Text Transfer Transformer (T5), designed to handle a wide range of NLP tasks using a unified text-to-text approach. Developed by researchers at Google, this model reframes all tasks—such as translation, summarization, classification, and question answering—into the format of input and output as plain text strings. With only 60 million parameters, T5-Small is compact and suitable for fast inference or deployment in constrained environments. It...
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