Search Results for "tuning"
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Showing 15 open source projects for "tuning"
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Unsloth-MLX
Bringing the Unsloth experience to Mac users via Apple's MLX framework
...It supports loading and training Hugging Face models with fine-tuning strategies like SFT, DPO, ORPO, and GRPO and even handles exporting models to formats like GGUF for downstream use, although some limitations apply with quantized models. Users can write and test training pipelines directly on macOS before scaling up, accelerating development cycles and lowering entry barriers for model refinement. -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. -
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MLJ
A Julia machine learning framework
MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about 200 machine learning models written in Julia and other languages. The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. These repositories live at the JuliaAI umbrella organization. -
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StabilityMatrix
Multi-Platform Package Manager for Stable Diffusion
...This approach is useful for model selection, prompt engineering, and benchmarking new checkpoints against baseline models under reproducible conditions. By turning ad-hoc tests into tracked experiments, StabilityMatrix reduces bias, surfaces subtle regressions, and accelerates iteration when tuning generative systems. -
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Backtrack Sampler
An easy-to-understand framework for LLM samplers
...It allows developers to create and test their own token generation strategies by providing a base structure for manipulating logits and probabilities, making it a flexible tool for those interested in fine-tuning the behavior of LLMs. -
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MLJBase.jl
Core functionality for the MLJ machine learning framework
Repository for developers that provides core functionality for the MLJ machine learning framework. MLJ is a Julia framework for combining and tuning machine learning models. This repository provides core functionality for MLJ. -
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Netty
An event-driven asynchronous network application framework
...Support for common protocols—HTTP/1.1, HTTP/2, WebSocket, and raw TCP/UDP—makes it a foundation for RPC frameworks, proxies, and messaging systems. Pluggable transports and native integrations (like epoll or kqueue where available) allow fine-tuning for different operating systems. With robust resource management, leak detection, and extensive testing, Netty is widely chosen when low latency, scalability, and control over the I/O stack are required. -
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Production-ready Docker PHP
Production-ready Docker images for PHP. Optimized for Laravel
serversideup/php is an optimized set of Docker Images for running PHP applications in production. Everything is designed around improving the developer experience with PHP and Docker. Gone are the days of configuring each environment differently, and gone are the days of trying to figure out why your code works in one environment and not the other. These images are highly optimized to run modern PHP applications, no matter where you want your application to run. Ditch FPM for a modern way of... -
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
EasyR1 is a streamlined training framework for building “R1-style” reasoning models from open-source LLMs with minimal boilerplate. It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments without wrestling infrastructure. It emphasizes memory-efficient training strategies so you can train long-context or reasoning-dense models on commodity GPUs. ... -
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Serverless Java container
A Java wrapper to run Spring, Spring Boot, Jersey, and other apps
The AWS Serverless Java Container library is a framework that allows developers to run existing or new Java web applications—built with frameworks such as Spring, Jersey, Spark, Struts—inside AWS Lambda with minimal modifications. It bridges the gap between traditional servlet or web-framework models and serverless functions by mapping HTTP events from API Gateway into requests your framework understands and routing responses back appropriately. This means you can keep much of your familiar... -
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OpenXava
Automatic frontend engine for Java
Automatic frontend engine for Java. Write just the domain classes in plain Java. Get a web application ready for production. -
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Dingo API
A RESTful API package for the Laravel and Lumen frameworks
...Much of the package comes preconfigured so that you can start building your API as soon as possible. You can use your .env file to configure most of the package, however, some finer tuning of the package will require you to either publish the configuration file (Laravel) or configure it in bootstrap/app.php (Lumen). You may also use the boot method of your AppServiceProvider. -
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PyTorch SimCLR
PyTorch implementation of SimCLR: A Simple Framework
...In situations like this, we take the models’ pre-trained weights, append a new classifier layer on top of it, and retrain the network. This is called transfer learning, and is one of the most used techniques in CV. Aside from a few tricks when performing fine-tuning (if the case), it has been shown (many times) that if training for a new task, models initialized with pre-trained weights tend to learn faster and be more accurate then training from scratch using random initialization. -
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HyperGAN
Composable GAN framework with api and user interface
A composable GAN built for developers, researchers, and artists. HyperGAN builds generative adversarial networks in PyTorch and makes them easy to train and share. HyperGAN is currently in pre-release and open beta. Everyone will have different goals when using hypergan. HyperGAN is currently beta. We are still searching for a default cross-data-set configuration. Each of the examples supports search. Automated search can help find good configurations. If you are unsure, you can start with... -
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Project moved to GitHub! https://github.com/carrot2/carrot2 Carrot2 is an Open Source Search Results Clustering Engine. It can automatically organize small collections of documents, e.g. search results, into thematic categories. Carrot2 integrates very well with both Open Source and proprietary search engines.
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