Search Results for "c source code" - Page 49
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Showing 2850 open source projects for "c source code"
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Automated Interpretability
Code for Language models can explain neurons in language models paper
The automated-interpretability repository implements tools and pipelines for automatically generating, simulating, and scoring explanations of neuron (or latent feature) behavior in neural networks. Instead of relying purely on manual, ad hoc interpretability probing, this repo aims to scale interpretability by using algorithmic methods that produce candidate explanations and assess their quality. It includes a “neuron explainer” component that, given a target neuron or latent feature,... -
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PyDenseCRF
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs
PyDenseCRF is a Python library that provides a wrapper around the implementation of fully connected Conditional Random Fields (CRFs) developed by Philipp Krähenbühl and Vladlen Koltun. The project allows developers and researchers to integrate Dense CRF inference into Python-based machine learning pipelines, particularly for computer vision tasks such as image segmentation and labeling. Conditional Random Fields are probabilistic graphical models used to model contextual relationships... -
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telegraf.js
Modern Telegram Bot Framework for Node.js
Bots are special Telegram accounts designed to handle messages automatically. Users can interact with bots by sending them command messages in private or group chats. These accounts serve as an interface for code running somewhere on your server. Telegraf is a library that makes it simple for you to develop your own Telegram bots using JavaScript or TypeScript. You can see in every example is a Context instance. Telegraf creates one for each incoming update and passes it to your middleware.... -
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MuJoCo MPC
Real-time behaviour synthesis with MuJoCo, using Predictive Control
MuJoCo MPC (MJPC) is an advanced interactive framework for real-time model predictive control (MPC) built on top of the MuJoCo physics engine, developed by Google DeepMind. It allows researchers and roboticists to design, visualize, and execute complex control tasks for simulated or real robotic systems. MJPC integrates a high-performance GUI and multiple predictive control algorithms, including iLQG, gradient descent, and Predictive Sampling — a competitive, derivative-free method that... -
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DeepSeek LLM
DeepSeek LLM: Let there be answers
The DeepSeek-LLM repository hosts the code, model files, evaluations, and documentation for DeepSeek’s LLM series (notably the 67B Chat variant). Its tagline is “Let there be answers.” The repo includes an “evaluation” folder (with results like math benchmark scores) and code artifacts (e.g. pre-commit config) that support model development and deployment. According to the evaluation files, DeepSeek LLM 67B Chat achieves strong performance on math benchmarks under both chain-of-thought (CoT)... -
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Bard API
The unofficial python package that returns response of Google Bard
The Python package returns a response of Google Bard through the value of the cookie. This package is designed for application to the Python package ExceptNotifier and Co-Coder. Please note that the bardapi is not a free service, but rather a tool provided to assist developers with testing certain functionalities due to the delayed development and release of Google Bard's API. It has been designed with a lightweight structure that can easily adapt to the emergence of an official API.... -
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iX
Autonomous GPT-4 agent platform
IX is a platform for designing and deploying autonomous and [semi]-autonomous LLM-powered agents and workflows. IX provides a flexible and scalable solution for delegating tasks to AI-powered agents. Agents created with the platform can automate a wide variety of tasks while running in parallel and communicating with each other. -
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FastChat
Open platform for training, serving, and evaluating language models
FastChat is an open platform for training, serving, and evaluating large language model-based chatbots. If you do not have enough memory, you can enable 8-bit compression by adding --load-8bit to the commands above. This can reduce memory usage by around half with slightly degraded model quality. It is compatible with the CPU, GPU, and Metal backend. Vicuna-13B with 8-bit compression can run on a single NVIDIA 3090/4080/T4/V100(16GB) GPU. In addition to that, you can add --cpu-offloading to... -
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TensorFlow Privacy
Library for training machine learning models with privacy for data
Library for training machine learning models with privacy for training data. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
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GPT-2
Code for the paper Language Models are Unsupervised Multitask Learners
This repository contains the code and model weights for GPT-2, a large-scale unsupervised language model described in the OpenAI paper “Language Models are Unsupervised Multitask Learners.” The intent is to provide a starting point for researchers and engineers to experiment with GPT-2: generate text, fine‐tune on custom datasets, explore model behavior, or study its internal phenomena. The repository includes scripts for sampling, training, downloading pre-trained models, and utilities for... -
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MGIE
Guiding Instruction-based Image Editing via Multimodal Large Language
MGIE—Guiding Instruction-based Image Editing—demonstrates how a multimodal LLM can parse natural-language editing instructions and then drive image transformations accordingly. The project focuses on making edits explainable and controllable: the model interprets text guidance, reasons over image content, and outputs edits aligned with user intent. It’s positioned as an ICLR 2024 Spotlight work, with code and references that show how to connect language planning to concrete image operations.... -
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Deep Learning Models
A collection of various deep learning architectures, models, and tips
This repository collects clear, well-documented implementations of deep learning models and training utilities written by Sebastian Raschka. The code favors readability and pedagogy: components are organized so you can trace data flow through layers, losses, optimizers, and evaluation. Examples span fundamental architectures—MLPs, CNNs, RNN/Transformers—and practical tasks like image classification or text modeling. Reproducible training scripts and configuration files make it... -
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Visual Studio Code client for Tabnine
Visual Studio Code client for Tabnine
This extension is for Tabnine’s Starter (free), Pro and Enterprise SaaS users only. Tabnine Enterprise users with the self-hosted setup should use the Tabnine Enterprise extension in the VSCode Marketplace. Tabnine is an AI code assistant that makes you a better developer. Tabnine will increase your development velocity with real-time code completions, chat, and code generation in all the most popular coding languages and IDEs. Whether you call it IntelliSense, intelliCode, autocomplete,... -
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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. -
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Plugins Quickstart
Get a ChatGPT plugin up and running in under 5 minutes
plugins-quickstart is a starter project created by OpenAI to help developers build and deploy ChatGPT plugins quickly. 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.... -
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OnnxStream
Lightweight inference library for ONNX files, written in C++
The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which... -
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Weak-to-Strong
Implements weak-to-strong learning for training stronger ML models
Weak-to-Strong is an OpenAI research codebase that implements the concept of weak-to-strong generalization, as described in the accompanying paper. The project provides tools for training larger “strong” models using labels or guidance generated by smaller “weak” models. Its core functionality focuses on binary classification tasks, with support for fine-tuning pretrained language models and experimenting with different loss functions, including confidence-based auxiliary losses. The... -
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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... -
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DeepSeek MoE
Towards Ultimate Expert Specialization in Mixture-of-Experts Language
DeepSeek-MoE (“DeepSeek MoE”) is the DeepSeek open implementation of a Mixture-of-Experts (MoE) model architecture meant to increase parameter efficiency by activating only a subset of “expert” submodules per input. The repository introduces fine-grained expert segmentation and shared expert isolation to improve specialization while controlling compute cost. For example, their MoE variant with 16.4B parameters claims comparable or better performance to standard dense models like DeepSeek 7B... -
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Obsei
Obsei is a low code AI powered automation tool
Obsei is an automated no-code/low-code AI-powered text observation and analysis framework, designed for extracting insights from unstructured text data such as social media, reviews, and logs. -
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Computer vision projects
computer vision projects | Fun AI projects related to computer vision
Computer vision projects is an open-source collection of computer vision projects and experiments that demonstrate practical applications of modern AI techniques in image processing, robotics, and real-time visual analysis. The repository includes multiple demonstration systems implemented using languages such as Python and C++, covering topics ranging from object detection to embedded vision systems. -
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CommandDash
AI assist to integrate APIs and SDKs without reading docs
Integrate any package, SDK, or framework with expert AI agents. Get contextualized code for your use case within the IDE. Modern software is built on top of 3rd party APIs and SDKs. However integrating them is time-consuming, requiring to manually read docs and copy-paste snippets. CommandDash enables you to skip reading documentation and integrate any API or SDK with an IDE agent up to date with the latest documentation, examples, and issues. -
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Critterding2
Evolving Artificial Life
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PromptTools
Open-source tools for prompt testing and experimentation
Welcome to prompttools created by Hegel AI! This repo offers a set of open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. The core idea is to enable developers to evaluate using familiar interfaces like code, notebooks, and a local playground. -
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Mixtral offloading
Run Mixtral-8x7B models in Colab or consumer desktops
Mixtral-Offloading is an open-source project designed to enable efficient inference of large Mixture-of-Experts language models such as Mixtral-8x7B on hardware with limited GPU memory. The project implements techniques that allow model components to be dynamically moved between CPU memory and GPU memory during inference, significantly reducing the amount of GPU VRAM required to run the model.
