Search Results for "hj-split"
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Showing 14 open source projects for "hj-split"
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Generative AI for Beginners (Version 3)
21 Lessons, Get Started Building with Generative AI
Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. ... -
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torchtext
Data loaders and abstractions for text and NLP
...LTS versions are distributed through a different channel than the other versioned releases. Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses. To build torchtext from source, you need git, CMake and C++11 compiler such as g++. When building from source, make sure that you have the same C++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext. If you are using the nightly build of PyTorch, check out the environment it was built with conda (here) and pip (here). ... -
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AIMET
AIMET is a library that provides advanced quantization and compression
Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware... -
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GluonTS
Probabilistic time series modeling in Python
...The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts, by removing the last three years (36 months) from the train data. Thus, we will train a model on just the first nine years of data. Python has the notion of extras – dependencies that can be optionally installed to unlock certain features of a package. We make extensive use of optional dependencies in GluonTS to keep the amount of required dependencies minimal. ... -
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PoseidonQ - AI/ML Based QSAR Modeling
ML based QSAR Modelling And Translation of Model to Deployable WebApps
- This Software was made with an intention to make QSAR building more efficient and reproducible. - Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372 - Simple to use and no compromise on essential features necessary to make reliable QSAR models. - From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp. For any feedback or queries, contact kabeermuzammil614@gmail.com - Available on Windows... -
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PRM800K
800,000 step-level correctness labels on LLM solutions to MATH problem
PRM800K is a process supervision dataset accompanying the paper Let’s Verify Step by Step, providing 800,000 step-level correctness labels on model-generated solutions to problems from the MATH dataset. The repository releases the raw labels and the labeler instructions used in two project phases, enabling researchers to study how human raters graded intermediate reasoning. Data are stored as newline-delimited JSONL files tracked with Git LFS, where each line is a full solution sample that... -
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unit-minions
AI R&D Efficiency Improvement Research: Do-It-Yourself Training LoRA
"AI R&D Efficiency Improvement Research: Do-It-Yourself Training LoRA", including Llama (Alpaca LoRA) model, ChatGLM (ChatGLM Tuning) related Lora training. Training content: user story generation, test code generation, code-assisted generation, text to SQL, text generation code. -
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OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. OGB provides a diverse set of challenging and realistic benchmark datasets that are of varying sizes and cover a variety graph machine learning tasks, including prediction of node, link, and graph properties. ... -
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Userge
Userge, Durable as a Serge
UserGe is a Powerful, Pluggable Telegram UserBot written in Python using Pyrogram by which you can Automate your Telegram account to work as you want. It comes with salient and descriptive features that help you to manage your task with some easy command. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Grade School Math
8.5K high quality grade school math problems
The grade-school-math repository (sometimes called GSM8K) is a curated dataset of 8,500 high-quality grade school math word problems intended for evaluating mathematical reasoning capabilities of language models. It is structured into 7,500 training problems and 1,000 test problems. These aren’t trivial exercises — many require multi-step reasoning, combining arithmetic operations, and handling intermediate steps (e.g. “If she sold half as many in May… how many in total?”). The problems are... -
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qiji-font
Typeface from Ming Dynasty woodblock printed books
...A work in progress. Named in honor of 閔齊伋, a 16th-century printer. Intended to be used with Kenyan-lang, the Classical Chinese programming language. Download high-resolution PDFs and split pages into images. Manually lay a grid on top of each page to generate bounding boxes for characters (potentially replaceable by an automatic corner-detection algorithm). Generate a low-poly mask for each character on the grid, and save the thumbnails (using OpenCV). First, red channel is subtracted from the grayscale, in order to clean the annotations printed in red ink. ... -
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VGGFace2
VGGFace2 Dataset for Face Recognition
...It consists of 3.31 million images covering 9,131 subjects, with an average of over 360 images per subject. The dataset was collected from Google Image Search, ensuring a wide diversity in ethnicity, profession, and real-world conditions. It is split into a training set with 8,631 identities and a test set with 500 identities, making it suitable for benchmarking and large-scale model training. Alongside the dataset, the repository provides pre-trained models based on ResNet-50 and SE-ResNet-50 architectures, trained with both MS-Celeb-1M pretraining and fine-tuning on VGGFace2. ... -
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DC-TTS
TensorFlow Implementation of DC-TTS: yet another text-to-speech model
...It follows the “Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention” paper, but the author adapts and extends the design to make it practical for real experiments. The model is split into two networks: Text2Mel, which maps text to mel-spectrograms, and SSRN (spectrogram super-resolution network), which converts low-resolution mel-spectrograms into high-resolution magnitude spectrograms suitable for waveform synthesis. Training scripts, data loaders, and hyperparameter configurations are provided to reproduce results on several datasets, including LJ Speech for English, a Korean single-speaker dataset, and audiobook data from Nick Offerman and Kate Winslet. -
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aCompute
Aims to enable researcher to tap in to mobile computing capability
This is a software agent based computing program that will enable researchers and other users to tap in computing power of machine available by sharing work load on the fly with zero configuration on network & resources A self organizing agent program that will understand network and its resource. where as the only job left to researcher is to split up jobs in several chunks of programs either parallel or sequential jobs and go issue the job (A visual Modeler or Scripting support need to be yet designed) Software agents will automatically manage the rest or resource management, sharing , cloning of tasks etc. new resources can be added and removed from the system on fly; in layman terms the project will create an agent program that enable sharing & execution of program among all the available resources whether it be desktop, laptop, pda . thereby one can accelerate research to the very extent of resource availability with out bothering about anything... ...
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