Search Results for "reasoning models"
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Showing 13 open source projects for "reasoning models"
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
...It emphasizes memory-efficient training strategies so you can train long-context or reasoning-dense models on commodity GPUs. The framework is also organized to help you compare training strategies (e.g., pure SFT vs. preference optimization) so you can see what actually moves metrics in math, code, and multi-step reasoning. For teams exploring open reasoning models, EasyR1 provides an opinionated yet flexible path from dataset to deployable checkpoints. -
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Ollama-Laravel Package
Ollama-Laravel is a Laravel package providing seamless integration
...It also includes support for reasoning models and function calling, enabling developers to build more advanced workflows where models can trigger tools or structured actions. Real-time streaming responses are supported, allowing applications to deliver incremental outputs for better user experience. -
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Synthetic Data Kit
Tool for generating high quality Synthetic datasets
Synthetic Data Kit is a CLI-centric toolkit for generating high-quality synthetic datasets to fine-tune Llama models, with an emphasis on producing reasoning traces and QA pairs that line up with modern instruction-tuning formats. It ships an opinionated, modular workflow that covers ingesting heterogeneous sources (documents, transcripts), prompting models to create labeled examples, and exporting to fine-tuning schemas with minimal glue code. -
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ARC-AGI
The Abstraction and Reasoning Corpus
ARC-AGI is a benchmark dataset and experimental framework designed to evaluate and advance artificial general intelligence by testing systems on abstract reasoning tasks that require human-like problem-solving abilities. It consists of a curated set of tasks where models must infer patterns from input-output examples and apply those rules to new unseen cases, without relying on memorization or prior training data. The dataset is structured as grid-based puzzles, where each task requires understanding transformations such as symmetry, counting, or spatial manipulation. ... -
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Page Agent
JavaScript in-page GUI agent. Control web interfaces
...Page Agent is designed to integrate seamlessly into existing web applications, making it possible to embed AI copilots into SaaS platforms without major backend changes. It supports a bring-your-own-LLM approach, allowing developers to connect their preferred language models to power the agent’s reasoning capabilities. -
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Cookbook (Google Gemini)
Examples and guides for using the Gemini API
...The repository covers a wide range of Gemini capabilities, including text, images, video, speech, robotics, and multimodal interactions. It highlights newly introduced features such as Gemini 2.5 models (Flash and Pro), Gemini’s native image generation, Veo for video generation, robotics-focused reasoning models, and Lyria for TTS and music generation. The Cookbook also includes tutorials on advanced API workflows such as grounding answers with external tools, batch-mode request handling, and live multimodal interactivity with LiveAPI. ... -
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Substrate
An Open-source Framework for Human Understanding, Meaning, Progress
Substrate is an open-source framework focused on human understanding, meaning, and progress. It aims to surface and structure conceptual objects—ideas, problems, beliefs, models, frames, goals, arguments, sources—to make them more transparent, discussable and actionable. The goal is to enable communities to collectively build and maintain a repository of these objects so that complex systems of meaning and progress can be mapped, analyzed and improved. It is relatively ambitious in... -
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JavaScript Questions
A long list of (advanced) JavaScript questions, and their explanations
...It covers tricky corners—hoisting, closures, coercion, event loop, prototypes, this binding, async/await, and more—through short prompts followed by illuminating answers. Explanations often include runnable snippets and step-through reasoning so you can replicate results locally. The content is curated to sharpen mental models rather than memorize trivia, making it valuable for interviews and day-to-day debugging alike. Because questions range from beginner to advanced, you can use it as a progressive study set or a quick refresher before assessments. It’s equally helpful for mentors: the questions can anchor study groups, workshops, or code-review discussions. -
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FOML is an expressive logic rule language that supports object modeling, analysis, and inference. It naturally supports model-level activities, such as constraints (extending UML diagrams), dynamic compositional modeling, analysis and reasoning about models, model testing, design pattern modeling, specification of Domain Specific Modeling Languages, and meta-modeling. FOML can reason about: 1. The model meta-data (meta-model level reasoning, or syntax reasoning) 2. Data level: reason about a model and its data (instance model, i.e., diagram) FOML is built as a semantic layer on top of PathLP - a compact logic rule language of guarded path expressions, an adaptation of a subset of F-logic.
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Solved by Flexbox
A showcase of problems once hard or impossible to solve with CSS
Solved by Flexbox is a collection of layout patterns that demonstrates how CSS Flexbox elegantly resolves classic web design problems. It tackles everyday challenges—vertical centering, equal-height columns, sticky footers, fluid media, and responsive grids—showing concise, production-ready CSS instead of elaborate hacks. Each pattern is presented with plain HTML, clear commentary, and minimal styling, making the underlying technique easy to adapt and extend. The project emphasizes... -
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Euler
A distributed graph deep learning framework.
...Data in the fields of text, speech, and images is easier to process into a grid-like type of Euclidean space, which is suitable for processing by existing deep learning models. Graph is a data type in non-Euclidean space and cannot be directly applied to existing methods, requiring a specially designed graph neural network system. Graph-based learning methods such as graph neural networks combine end-to-end learning with inductive reasoning, and are expected to solve a series of problems such as relational reasoning and interpretability that deep learning cannot handle. -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
...Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo provides training recipes and models for standard datasets, as well as ablations that show how many non-local blocks to insert and at which stages. Efficient implementations keep memory and compute manageable so the blocks can be added without rewriting the entire backbone. ... -
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DBNL
Dynamic Bayesian Network Library
DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary questions given the provided evidence and can even cope with multimodal densities. ...
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