Search Results for "fit"
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Showing 52 open source projects for "fit"
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FIT Framework
An enterprise-level AI development framework
FIT Framework is an open-source infrastructure designed to support the development, training, and evaluation of machine learning and AI models through a modular and scalable architecture. It aims to streamline the lifecycle of AI systems by providing standardized components for data processing, model training, evaluation, and deployment. -
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imodelsX
Interpretable prompting and models for NLP
...Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better features via LLMs, but at test-time, they are extremely fast and completely transparent. -
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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. ... -
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Continue
Continue is the leading open-source AI code assistant
...Continue enables you to use the right model for the job, whether it's open-source or commercial, running local or remote, and used for chat, autocomplete, or embeddings. And we provide numerous points of configuration so that you can customize the extension to fit into your existing workflows. -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
...It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. TFP is open source and available on GitHub. Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction. Variational inference and Markov chain Monte Carlo. A wide selection of probability distributions and bijectors. ... -
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FSRS4Anki
A modern Anki custom scheduling based on Free Spaced Repetition
A modern spaced-repetition scheduler for Anki based on the Free Spaced Repetition Scheduler algorithm. -
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InterpretML
Fit interpretable models. Explain blackbox machine learning
In the beginning, machines learned in darkness, and data scientists struggled in the void to explain them. InterpretML is an open-source package that incorporates state-of-the-art machine-learning interpretability techniques under one roof. With this package, you can train interpretable glass box models and explain black box systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. -
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agents-cli
CLI to turn coding assistants into expert at deploying AI agents
...The tool integrates with agent frameworks and supports modular extensions for adding new capabilities. It emphasizes productivity by enabling rapid iteration and testing of agent logic without complex setup. agents-cli is designed to fit into modern developer workflows, particularly those that rely on automation and scripting. It allows users to orchestrate tasks, manage configurations, and monitor execution in a streamlined environment. Overall, it provides a developer-friendly entry point into agent-based systems. -
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Implicit
Fast Python collaborative filtering for implicit feedback datasets
This project provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets. All models have multi-threaded training routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores. In addition, the ALS and BPR models both have custom CUDA kernels - enabling fitting on compatible GPU’s. This library also supports using approximate nearest neighbour libraries such as Annoy, NMSLIB and Faiss for speeding up making recommendations. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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GitHub Copilot SDK
Multi-platform SDK for integrating GitHub Copilot Agent into apps
...Instead of being limited to editors like VS Code, this SDK lets teams embed Copilot-style code suggestions, natural language assistance, and predictive completions anywhere they see fit—such as internal IDEs, browser extensions, documentation portals, or bespoke tools tailored to specific languages or frameworks. It provides a structured API surface for invoking the Copilot model in context with the surrounding user state, capturing document content, cursor position, and invocation triggers so suggestions are relevant and responsive. ... -
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DataFrame
C++ DataFrame for statistical, Financial, and ML analysis
...DataFrame also includes a large collection of analytical algorithms in the form of visitors. These are from basic stats such as Mean, and Std Deviation and return, … to more involved analysis such as Affinity Propagation, Polynomial Fit, and Fast Fourier transform of arbitrary length … including a good collection of trading indicators. You can also easily add your own algorithms. -
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marimo
A reactive notebook for Python
marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots, make working with data feel refreshingly fast, futuristic, and intuitive. Version with git, run as Python scripts, import symbols from a notebook into other notebooks or Python files, and lint or format with your favorite tools. ... -
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whichllm
Find the local LLM that actually runs and performs best
whichllm is a command-line tool for finding local large language models that can realistically run on a user’s hardware. It detects the machine’s available resources, including GPU, CPU, memory, and storage, then recommends models based on practical fit rather than parameter count alone. The project is useful for users who are unsure which local LLM will perform well on their system. It focuses on real, recency-aware benchmarks so recommendations better reflect current model performance. whichllm is especially helpful for developers, AI hobbyists, and researchers comparing local inference options across NVIDIA, AMD, Apple Silicon, and CPU-only environments. ... -
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Z80-μLM
Z80-μLM is a 2-bit quantized language model
...The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment. -
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SuperDesign
AI Product Design Agent
...This tool is designed to support modularity and consistency, helping teams maintain a coherent design system without manually crafting every element. Because it is open source, anyone can inspect, extend, and customize the agent to fit specific workflows. -
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gplearn
Genetic Programming in Python, with a scikit-learn inspired API
gplearn implements Genetic Programming in Python, with a scikit-learn-inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straightforward to implement. Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best... -
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i.am.ai
Roadmap to becoming an Artificial Intelligence Expert in 2022
...The roadmap emphasizes foundational skills like mathematics, programming, and data handling before progressing into deep learning and specialized domains. Rather than prescribing a single path, it helps users navigate the AI landscape and understand which tools fit different scenarios. Overall, the repository serves as a high-level strategic learning map for individuals planning long-term AI careers. -
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Ralph
Autonomous AI agent loop that runs until all PRD items are complete
...Each run selects the highest-priority unfinished user story, implements it, runs quality checks, commits passing work, and updates the PRD status. The workflow encourages teams to split features into small, verifiable stories that fit inside a single context window. It also supports PRD generation skills, PRD-to-JSON conversion, Claude Code marketplace installation, browser verification for UI stories, and automatic archiving of previous feature runs. -
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Gollama
Go manage your Ollama models
...One of its more distinctive capabilities is a VRAM estimation system that can calculate memory requirements, estimate context limits, and help users choose quantization settings that fit available hardware. -
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PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
...The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. It’s aimed at developers who want a transparent, hackable research agent they can run locally or wire into existing workflows. -
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AutoMLPipeline.jl
Package that makes it trivial to create and evaluate machine learning
AutoMLPipeline (AMLP) is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, and manipulate pipeline expressions and makes it easy to discover optimal structures for machine learning regression and classification. To illustrate, here is a pipeline expression and evaluation of a typical machine learning workflow that extracts numerical features (numf) for... -
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Vald
Vald. A Highly Scalable Distributed Vector Search Engine
...But Vald uses distributed index graphs so it continues to work during indexing. Vald implements it's own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery. -
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CAG
Cache-Augmented Generation: A Simple, Efficient Alternative to RAG
...As a result, the approach can significantly reduce latency and simplify system architecture compared with traditional RAG pipelines. The framework is particularly effective when the knowledge base is limited enough to fit within the extended context window of modern language models. -
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StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery... -
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NeuralForecast
Scalable and user friendly neural forecasting algorithms.
NeuralForecast offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like RNNs to the latest transformers: MLP, LSTM, GRU, RNN, TCN, TimesNet, BiTCN, DeepAR, NBEATS, NBEATSx, NHITS, TiDE, DeepNPTS, TSMixer, TSMixerx, MLPMultivariate, DLinear, NLinear, TFT, Informer, AutoFormer, FedFormer, PatchTST, iTransformer, StemGNN, and TimeLLM. There is a shared belief in Neural forecasting methods'...
