Search Results for "without code" - Page 11
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Showing 379 open source projects for "without code"
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AIConfig
AIConfig is a config-based framework to build generative AI apps
...AIConfig supports multiple model providers and modalities, enabling developers to experiment with different models without rewriting application logic. The configuration format is JSON-serializable and integrates with tools such as Python and Node SDKs, allowing the same configuration file to be used across multiple environments. -
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sketch
AI code-writing assistant that understands data content
Sketch is an open-source AI-powered data analysis assistant designed specifically for pandas users, enabling natural language interaction with tabular datasets to generate code, insights, and transformations. It works by summarizing the structure and statistical properties of a dataset and providing that context to a language model, allowing it to generate highly relevant and accurate responses tailored to the data. The tool integrates directly into pandas dataframes through an extension, making it easy to use within existing Python workflows without requiring additional IDE plugins. ... -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without changing the code base. ... -
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Plugins Quickstart
Get a ChatGPT plugin up and running in under 5 minutes
...It provides a minimal but complete example of how to structure a plugin, implement an API, and define the necessary configuration files. The repository demonstrates how a plugin can be served, authenticated, and integrated with ChatGPT for real-world use. By including both the backend code and plugin manifest, it guides developers through the end-to-end development workflow. This makes it a useful resource for those experimenting with extending ChatGPT capabilities or adding custom functionality to their own workflows. Designed to be simple and approachable, plugins-quickstart allows developers to learn plugin mechanics without dealing with unnecessary complexity. -
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Style Aligned
Official code for Style Aligned Image Generation via Shared Attention
StyleAligned is a diffusion-model editing technique and codebase that preserves the visual “style” of an original image while applying new semantic edits driven by text. Instead of fully re-generating an image—and risking changes to lighting, texture, or rendering choices—the method aligns internal features across denoising steps so the target edit inherits the source style. This alignment acts like a constraint on the model’s evolution, steering composition, palette, and brushwork even as... -
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eCxx
A C++ library for AVR and NodeMCU
NOTE: This project is marked with 'Status: Abandoned' on SourceForge because not enough time can be dedicated to this project. However it may still get sporadic commits to the repository. eCxx is a library for AVR and NodeMCU tailored for micro LED displays and lighting effects. eCxx is utilizing Makefile build system. Java and Python based applications/tools are also included to ease the development and debugging process using the host PC. On one side, eCxx supports the original... -
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Sweep AI
Sweep: AI-powered Junior Developer for small features and bug fixes
Let Sweep handle your tech debt so you can focus on the exciting problems. Sweep is an AI junior developer that transforms bug reports & feature requests into code changes. Describe bugs, small features, and refactors like you would to a junior developer and Sweep. -
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ComfyUI Experiments
Some experimental custom nodes
ComfyUI_experiments is a playground repo for trying out new, sometimes unstable ideas in the ComfyUI ecosystem before they graduate into more official nodes or workflows. It’s where experimental nodes, pipelines, or integrations can live without breaking users’ main installations. The project is aimed at power users and contributors who want to see “what’s possible” with ComfyUI beyond the stable set of features. Because it is exploratory, the code may change often, rely on specific versions, or require manual setup, which is why it’s separated from the main ComfyUI codebase. ... -
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fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. The framework enables users to test common strategies such as moving average crossovers, momentum trading, and custom indicators on historical stock data. By automating data retrieval, strategy evaluation, and result visualization, the library reduces the barrier to entry for individuals interested in quantitative finance. ... -
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Basaran
Basaran, an open-source alternative to the OpenAI text completion API
...The open source community will eventually witness the Stable Diffusion moment for large language models (LLMs), and Basaran allows you to replace OpenAI's service with the latest open-source model to power your application without modifying a single line of code. Stream generation using various decoding strategies. Support both decoder-only and encoder-decoder models. Detokenizer that handles surrogates and whitespace. Multi-GPU support with optional 8-bit quantization. Real-time partial progress using server-sent events. Compatible with OpenAI API and client libraries. ... -
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Detic
Code release for "Detecting Twenty-thousand Classes
Detic (“Detecting Twenty-thousand Classes using Image-level Supervision”) is a large-vocabulary object detector that scales beyond fully annotated datasets by leveraging image-level labels. It decouples localization from classification, training a strong box localizer on standard detection data while learning classifiers from weak supervision and large image-tag corpora. A shared region proposal backbone feeds a flexible classification head that can expand to tens of thousands of categories... -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own... -
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CodeFormer
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
CodeFormer is a face restoration project based on the NeurIPS 2022 paper “Towards Robust Blind Face Restoration with Codebook Lookup Transformer.” It is designed to improve degraded, old, low-quality, or AI-generated face images without requiring a clean reference image. The model uses a codebook lookup transformer approach to balance perceptual quality and identity fidelity through a configurable fidelity weight. It supports cropped face restoration, whole-image enhancement, video enhancement, face colorization, and face inpainting. The repository provides pretrained model download scripts, inference commands, training code, and configuration files. ... -
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ChatGLM Efficient Tuning
Fine-tuning ChatGLM-6B with PEFT
ChatGLM-Efficient-Tuning is a hands-on toolkit for fine-tuning ChatGLM-family models with parameter-efficient methods on everyday hardware. It wraps techniques like LoRA and prompt-tuning into simple training scripts so you can adapt a large model to your domain without full retraining. The project exposes practical switches for quantization and mixed precision, allowing bigger models to fit into limited VRAM. It includes examples for instruction tuning and dialogue datasets, making it straightforward to stand up a task-specific assistant. Because the code leans on widely used libraries, you can bring your own datasets and monitoring tools with minimal glue. ... -
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Bard
Python SDK/API for reverse engineered Google Bard
The Bard repository from acheong08 is an unofficial client library and set of tools designed to interact programmatically with Google Bard, Google’s conversational AI service, allowing developers to automate queries, integrate Bard into their own applications, and experiment with scripting around Bard-style prompts without relying on a formal, official API. Because Google doesn’t provide a standard public API for Bard, this project works by reverse-engineering the web interactions that power Bard’s interface and exposing them through code-friendly interfaces, enabling chat automation, custom workflows, and bot creation. The repository typically includes authentication handling, session management, and request/response serialization so that developers don’t have to deal with low-level HTTP details. ... -
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Audio Webui
A webui for different audio related Neural Networks
Audio Webui is a Gradio-based web user interface that unifies a wide range of audio-related neural networks under a single, accessible front end. It is designed as an “all-in-one” environment where users can experiment with text-to-speech, voice cloning, generative music, and other neural audio models without writing boilerplate code. The project supports multiple back-end models and toolchains (such as Bark, RVC, AudioLDM, Audiocraft, and other text-to-audio or voice-cloning tools), exposing them through a consistent UI for inference and experimentation. Installation is streamlined through automatic installers and platform-specific scripts that create a virtual environment, install dependencies, and launch the web app with minimal manual setup. ... -
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iJEPA
Official codebase for I-JEPA
i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer... -
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ParlAI
A framework for training and evaluating AI models
...It provides a unified interface—agents, teachers, and worlds—so the same model can be trained on multi-turn chit-chat, question answering, task-oriented dialogue, retrieval, or safety-focused datasets without changing core code. The library integrates tightly with PyTorch and supports both generative and retrieval-augmented models, along with utilities for multitask training and model selection. A large set of built-in tasks and dataset loaders (with consistent preprocessing and metrics) makes it easy to compare methods under shared conditions. ... -
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minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
minimalRL is a lightweight reinforcement learning repository that implements several classic algorithms using minimal PyTorch code. The project is designed primarily as an educational resource that demonstrates how reinforcement learning algorithms work internally without the complexity of large frameworks. Each algorithm implementation is contained within a single file and typically ranges from about 100 to 150 lines of code, making it easy for learners to inspect the entire implementation at once. ... -
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Swiple
Swiple enables you to easily observe, understand, validate data
...With automated data analysis and profiling, scheduling and alerting, teams can resolve data quality issues before they impact mission critical resources. Experience hassle-free integration with Swiple's zero-infrastructure and zero-code setup. Seamlessly incorporate data quality checks into your existing workflows without any coding or infrastructure changes, allowing you to focus on what matters most - your data. Save engineers weeks of time generating data quality checks. Swiple analyzes your dataset and builds data quality checks based on what is observed in your data. ... -
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Bot on Anything
Large model-based chatbot builder that can quickly integrate AI models
...Configuration is handled simply through a central JSON file where you define which model and which application channel you want to glue together, so developers can create sophisticated AI assistants without rewriting integration code from scratch. The architecture emphasizes reusability and extensibility, allowing the addition of new model backends or new channels with relative ease. It supports switching between multiple AI models and targets within the same project. -
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AWS Jupyter Proxy
Jupyter server extension to proxy requests with AWS SigV4 authentican
A Jupyter server extension to proxy requests with AWS SigV4 authentication. This server extension enables the usage of the AWS JavaScript/TypeScript SDK to write Jupyter frontend extensions without having to export AWS credentials to the browser. A single /awsproxy endpoint is added on the Jupyter server which receives incoming requests from the browser, uses the credentials on the server to add SigV4 authentication to the request, and then proxies the request to the actual AWS service endpoint. All requests are proxied back-and-forth as-is, e.g., a 4xx status code from the AWS service will be relayed back as-is to the browser. ... -
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minGPT
A minimal PyTorch re-implementation of the OpenAI GPT
...It provides a practical sandbox for experimentation, letting learners tweak the architecture, dataset, or training loop without being overwhelmed by framework abstraction. -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
...The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large computational tasks into smaller chunks that can be executed across multiple nodes in a cluster, allowing complex analytics, machine learning workflows, and data transformations to run efficiently at scale. Mars is particularly useful for workloads that exceed the memory capacity of a single machine or require high levels of parallel processing. -
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CommandlineConfig
A library for users to write configurations in Python
CommandlineConfig is a lightweight Python library designed to simplify managing configuration parameters for experiments and applications, especially in research workflows that require frequent tweaking of hyperparameters. It lets you define configuration in familiar Python dictionaries or JSON files and then access nested parameters via dot notation in code, improving readability and reducing boilerplate. One of its core strengths is the ability to override configuration values directly from the command line, making it convenient to run many experimental variants without editing files repeatedly. The library supports arbitrarily deep nested structures, type handling, enumerated value constraints, and even tuple types, which are common in ML experiment setups. ...