Search Results for "data quality cleansing"
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14 projects for "data quality cleansing" with 2 filters applied:
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an open-vocabulary concept specified by a short phrase or exemplars, scaling to a vastly larger set of categories than traditional closed-set models. This capability is grounded in a new data engine that automatically annotated over four million unique concepts, producing a massive open-vocabulary segmentation dataset and enabling the model to achieve 75–80% of human performance on the SA-CO benchmark, which itself spans 270K unique concepts. -
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DeepSeek-V3.2-Exp
An experimental version of DeepSeek model
DeepSeek-V3.2-Exp is an experimental release of the DeepSeek model family, intended as a stepping stone toward the next generation architecture. The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely... -
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Ling-V2
Ling-V2 is a MoE LLM provided and open-sourced by InclusionAI
...It introduces highly sparse architectures where only a fraction of the model’s parameters are activated per input token, enabling models like Ling-mini-2.0 to achieve reasoning and instruction-following capabilities on par with much larger dense models while remaining significantly more computationally efficient. Trained on more than 20 trillion tokens of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-V2’s models demonstrate strong general reasoning, mathematical problem-solving, coding understanding, and knowledge-intensive task performance. -
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DINOv3
Reference PyTorch implementation and models for DINOv3
DINOv3 is the third-generation iteration of Meta’s self-supervised visual representation learning framework, building upon the ideas from DINO and DINOv2. It continues the paradigm of learning strong image representations without labels using teacher–student distillation, but introduces a simplified and more scalable training recipe that performs well across datasets and architectures. DINOv3 removes the need for complex augmentations or momentum encoders, streamlining the pipeline while... -
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TimesFM
Pretrained time-series foundation model developed by Google Research
...The repository also documents how model versions evolved, with newer variants focusing on efficiency and longer context windows while maintaining forecasting quality. -
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JoyAI-VL-Interaction
An Open Real-time Video-Language Interaction System
...It is designed to watch a webcam or livestream continuously and decide whether to speak, stay silent, or delegate a harder task. Unlike turn-based assistants, it focuses on event-driven interaction where timing matters as much as answer quality. The repository releases the model, training recipe, time-aligned interaction data, and deployable system together. Its system includes inference, WebUI, ASR, TTS, and background-agent services running on vLLM-based infrastructure. It is useful for real-time monitoring, live commentary, cooking guidance, game calling, visual alerts, and other scenarios where an AI should respond at the right moment. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
DINOv2 is a self-supervised vision learning framework that produces strong, general-purpose image representations without using human labels. It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. -
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Universal Sentence Encoder
Encoder of greater-than-word length text trained on a variety of data
The Universal Sentence Encoder (USE) is a pre-trained deep learning model designed to encode sentences into fixed-length embeddings for use in various natural language processing (NLP) tasks. It leverages Transformer and Deep Averaging Network (DAN) architectures to generate embeddings that capture the semantic meaning of sentences. The model is designed for tasks like sentiment analysis, semantic textual similarity, and clustering, and provides high-quality sentence representations in a... -
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GLM-4-32B-0414
Open Multilingual Multimodal Chat LMs
...It supports multilingual and multimodal chat capabilities with an extensive 32K token context length, making it ideal for dialogue, reasoning, and complex task completion. The model is pre-trained on 15 trillion tokens of high-quality data, including substantial synthetic reasoning datasets, and further enhanced with reinforcement learning and human preference alignment for improved instruction-following and function calling. Variants like GLM-Z1-32B-0414 offer deep reasoning and advanced mathematical problem-solving, while GLM-Z1-Rumination-32B-0414 specializes in long-form, complex research-style writing using scaled reinforcement learning and external search tools. ... -
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Consistency Models
Official repo for consistency models
consistency_models is the repository for Consistency Models, a new family of generative models introduced by OpenAI that aim to generate high-quality samples by mapping noise directly into data — circumventing the need for lengthy diffusion chains. It builds on and extends diffusion model frameworks (e.g. based on the guided-diffusion codebase), adding techniques like consistency distillation and consistency training to enable fast, often one-step, sample generation. The repo is implemented in PyTorch and includes support for large-scale experiments on datasets like ImageNet-64 and LSUN variants. ... -
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PRM800K
800,000 step-level correctness labels on LLM solutions to MATH problem
...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 can contain many step-level labels and rich metadata such as labeler UUIDs, timestamps, generation identifiers, and quality-control flags. Each labeled step can include multiple candidate completions with ratings of -1, 0, or +1, optional human-written corrections (phase 1), and a chosen completion index, along with a final finish reason such as found_error, solution, bad_problem, or give_up. -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the... -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs—think billions of nodes and edges—using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions,... -
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GigaChat 3 Ultra
High-performance MoE model with MLA, MTP, and multilingual reasoning
...Its training corpus incorporates ten languages, enriched with books, academic sources, code datasets, mathematical tasks, and more than 5.5 trillion tokens of high-quality synthetic data. This combination significantly boosts reasoning, coding, and multilingual performance across modern benchmarks. Designed for high-performance deployment, GigaChat 3 Ultra supports major inference engines and offers optimized BF16 and FP8 execution paths for cluster-grade hardware.
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