Search Results for "bloom"
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Showing 37 open source projects for "bloom"
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Bloom
An open source, agentic Loom alternative
Bloom is an open-source, agent-centric video recording and processing platform designed as a modern alternative to tools like Loom, but with a strong emphasis on AI-driven workflows and programmability. It allows users to record sessions locally while simultaneously preparing those recordings to be processed, indexed, and analyzed by AI systems, effectively turning simple screen captures into structured, queryable data. -
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Bloom filters
Go package implementing Bloom filters
A Bloom filter is a concise/compressed representation of a set, where the main requirement is to make membership queries; i.e., whether an item is a member of a set. A Bloom filter will always correctly report the presence of an element in the set when the element is indeed present. A Bloom filter can use much less storage than the original set, but it allows for some 'false positives': it may sometimes report that an element is in the set whereas it is not. -
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bert4torch
An elegent pytorch implement of transformers
An elegant PyTorch implement of transformers. -
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LLaMA Efficient Tuning
Easy-to-use LLM fine-tuning framework (LLaMA-2, BLOOM, Falcon
Easy-to-use LLM fine-tuning framework (LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, ChatGLM2) -
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Huatuo-Llama-Med-Chinese
Instruction-tuning LLM with Chinese Medical Knowledge
Huatuo-Llama-Med-Chinese is an open-source project that develops medical-domain large language models by instruction-tuning existing models using Chinese medical knowledge. The project builds specialized models by fine-tuning architectures such as LLaMA, Alpaca-Chinese, and Bloom with curated medical datasets. These datasets are constructed from medical knowledge graphs, academic literature, and question-answer pairs designed to teach models how to respond accurately to healthcare-related queries. The goal of the project is to improve the reliability and domain expertise of language models when answering medical questions or assisting with healthcare-related tasks. ... -
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CTranslate2
Fast inference engine for Transformer models
CTranslate2 is a C++ and Python library for efficient inference with Transformer models. The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many... -
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Pinot
Apache Pinot - A realtime distributed OLAP datastore
...Pinot is designed to answer OLAP queries with low latency on immutable data and mutable data(Upsert Support) Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index, StarTree Index, Bloom Filter, Range Index, Text Search Index(Lucence/FST), Json Index, Geospatial Index. Near Realtime ingestion with Apache Kafka, Apache Pulsar, Kinesissupports JSON, Avro, ProtoBuf, Thrift formats. Apache Pinot supports dimension lookup joins currently. Full SQL join support in Apache Pinot is coming soon. In the interim, full SQL joins can be performed by querying Pinot via the Trino or PrestoDB connector. ... -
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Apache HBase
Get random, realtime read/write access to your Big Data
Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables, billions of rows X millions of columns, atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable. A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache... -
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Machine Learning Engineering Open Book
Machine Learning Engineering Open Book
Machine Learning Engineering Open Book is an open “living book” that captures practical methodologies, tooling advice, and operational knowledge for successfully training and deploying large language models and multimodal systems. The repository functions as a field guide compiled from real-world experience, particularly from work on large-scale models such as BLOOM-176B and IDEFICS-80B. It is heavily oriented toward practitioners who need hands-on solutions, including copy-paste commands, infrastructure comparisons, and performance tuning strategies. The material spans the full ML lifecycle, from hardware selection and distributed training to inference optimization and debugging. Rather than focusing purely on theory, the project emphasizes engineering tradeoffs and production realities that often determine success at scale. ... -
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xxHash
Extremely fast non-cryptographic hash algorithm
...Code is highly portable, and hashes are identical across all platforms (little / big endian). Performance on large data is only one part of the picture. Hashing is also very useful in constructions like hash tables and bloom filters. In these use cases, it's frequent to hash a lot of small data (starting at a few bytes). Algorithm's performance can be very different for such scenarios, since parts of the algorithm, such as initialization or finalization, become fixed cost. The impact of branch misprediction also becomes much more present. -
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DeepSpeed MII
MII makes low-latency and high-throughput inference possible
MII makes low-latency and high-throughput inference possible, powered by DeepSpeed. The Deep Learning (DL) open-source community has seen tremendous growth in the last few months. Incredibly powerful text generation models such as the Bloom 176B, or image generation model such as Stable Diffusion are now available to anyone with access to a handful or even a single GPU through platforms such as Hugging Face. While open-sourcing has democratized access to AI capabilities, their application is still restricted by two critical factors: inference latency and cost. DeepSpeed-MII is a new open-source python library from DeepSpeed, aimed towards making low-latency, low-cost inference of powerful models not only feasible but also easily accessible. ... -
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Bloxshade
Bloxshade installer source code.
Improve Roblox with shaders. Bloxshade is a graphics enhancement tool designed to improve the visual quality of Roblox games through advanced shaders and post-processing effects. It allows players to customize lighting, color grading, reflections, and other visual elements to create a more immersive gaming experience. Built around NVIDIA Ansel and shader technology, Bloxshade transforms standard Roblox environments into more realistic and cinematic worlds. The platform offers customizable... -
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QuakeIIxp is a multi-platform (windows, linux and freeBSD (experemental)) graphics port of the game Quake II developed by Id Software. Completely updated rendering takes full advantage of the latest graphics cards to get the perfect picture, preserving the original style of the game.
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hash_val_4_Bloom
search for hash values used in Bloom filter for QNR membership
FenderBender -- on MersenneForum.org "Determine Squares" -- came up with using Bloom filter to detect Quadratic non-residues modulo small primes in order to reject what inputs are certainly not square roots. This reduces, by > 99%, the amount of actual square root calc's done and therefore is more efficient and faster. Search fur 32-bit vs 64-bit, hash values that make "perfect" Bloom filter. Variety of (simple) hash functions, requiring 2, 3, 4 or 5 hash values. -
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YAYI
Repo for YaYi Chinese LLMs based on LlaMA2 & BLOOM
YAYI is an open-source large language model project developed to provide a multilingual conversational AI system capable of performing a wide variety of natural language processing tasks. The model is trained on diverse datasets covering multiple languages and domains so that it can support applications ranging from dialogue systems to text analysis and knowledge retrieval. The architecture is based on transformer-style language models optimized for conversational understanding and... -
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Petals
Run 100B+ language models at home, BitTorrent-style
...Beyond classic language model APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch. You can also host BLOOMZ, a version of BLOOM fine-tuned to follow human instructions in the zero-shot regime — just replace bloom-petals with bloomz-petals. Petals runs large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. -
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GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
...It is designed for large-scale inference and supports both left-to-right generation and blank filling, making it versatile across NLP tasks. Trained on over 400 billion tokens (200B English, 200B Chinese), it achieves performance surpassing GPT-3 175B, OPT-175B, and BLOOM-176B on multiple benchmarks, while also showing significant improvements on Chinese datasets compared to other large models. The model supports efficient inference via INT8 and INT4 quantization, reducing hardware requirements from 8× A100 GPUs to as little as a single server with 4× RTX 3090s. Built on the SwissArmyTransformer (SAT) framework and compatible with DeepSpeed and FasterTransformer, it supports high-speed inference (up to 2.5× faster) and reproducible evaluation across 30+ benchmark tasks. -
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Algebird
Abstract Algebra for Scala
Algebird is Twitter’s Apache‑licensed Scala library providing abstract algebra data structures and algorithms, especially for online/streaming aggregation. It includes Monoid, Approximate, HyperLogLog, CMS, BloomFilter, Min/Max, Averaged Value types, supporting efficient distributed aggregation and approximate analytics. -
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Feathr
A scalable, unified data and AI engineering platform for enterprise
Feathr is a data and AI engineering platform that is widely used in production at LinkedIn for many years and was open sourced in 2022. It is currently a project under LF AI & Data Foundation. Define data and feature transformations based on raw data sources (batch and streaming) using Pythonic APIs. Register transformations by names and get transformed data(features) for various use cases including AI modeling, compliance, go-to-market and more. Share transformations and data(features)... -
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Mire Engine
2D Game Engine
A Cross Platform 2d Rpg Game Engine for mobile and Console. -
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openfx-arena
Extra OpenFX plugins for Natron/Nuke/Resolve
Extra OpenFX plugins for Natron/Nuke/Resolve. -
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XPL
Unity Post Processing Stack Library
The XPL Library project is a collection of advanced post-processing effects designed for use in Unity projects. It provides a wide range of visual effects such as bloom, color grading, motion blur, and stylized filters, enabling developers to enhance the visual quality of their games. The library is built with performance in mind, ensuring that effects can be applied efficiently in real-time environments. It integrates seamlessly with Unity’s rendering pipeline, allowing developers to easily incorporate and customize effects. ... -
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3D Game Shaders for Beginners
A step-by-step guide to implementing SSAO for 3D games
Interested in adding textures, lighting, shadows, normal maps, glowing objects, ambient occlusion, reflections, refractions, and more to your 3D game? Great! Below is a collection of shading techniques that will take your game visuals to new heights. I've explained each technique in such a way that you can take what you learn here and apply/port it to whatever stack you use—be it Godot, Unity, Unreal, or something else. For the glue in between the shaders, I've chosen the fabulous Panda3D... -
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Tera
An Internet-scale database
...The table is indexed by a row key, column key, and a timestamp; each value in the table is an uninterpreted array of bytes. Support RAMDISK/SSD/DFS tiered cache. Block cache and Bloom Filters for real-time queries. Multi-type table support (RAMDISK/SSD/DISK table). Easy to use C++/Java/Python/REST-ful API. Column-oriented storage and locality group support. Ranged and hashed sharding strategies. -
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Subtractive Assembly
An approach for comparative metagenomics by assembling the differences
...Given two metagenomic datasets A and B, subtractive assembly approach first identifies reads that are likely sampled from abundant or unique genomes in A (or B vice versa), using Bloom-filter based k-mer counting. And one can assemble only these reads separately using metagenomic assemblers such as IDBA-UD. We further developed concurrent subtractive assembly (CoSA), which is an improved version of the subtractive assembly approach for detecting differential reads between two groups of metagenomes. Please check CoSA out at https://sourceforge.net/projects/concurrentsa/...