Search Results for "artificial intelligence algorithm" - Page 3
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Showing 215 open source projects for "artificial intelligence algorithm"
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LaMDA-pytorch
Open-source pre-training implementation of Google's LaMDA in PyTorch
Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model. -
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GPT Neo
An implementation of model parallel GPT-2 and GPT-3-style models
An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here to play with our pre-trained models, we strongly recommend you try out the HuggingFace Transformer integration. Training and inference is officially supported on TPU and should work on GPU as well. This repository will be (mostly) archived as we move focus to our GPU-specific repo, GPT-NeoX. NB, while neo can technically run a training step at 200B+ parameters, it is very... -
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DeepSWE-Preview
State-of-the-art RL-trained coding agent for complex SWE tasks
DeepSWE-Preview is a 32.8B parameter open-source coding agent trained solely with reinforcement learning (RL) to perform complex software engineering (SWE) tasks. Built on top of Qwen3-32B, it achieves 59% accuracy on the SWE-Bench-Verified benchmark—currently the highest among open-weight models. The model navigates and edits large codebases using tools like a file editor, bash execution, and search, within the R2E-Gym environment. Its training emphasizes sparse reward signals, test-time... -
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Dark GPT Free
Free chat GPT app without censorship!
This is the same GPT chat, but there are no restrictions and you can ask whatever you want. ATTENTION, Chrome blocks downloading .zip files, so to download our application you need to open the download page in Microsoft Edge or another browser except Chrome. Good luck! -
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unidepth-v2-vitl14
Metric monocular depth estimation (vision model)
Estimates absolute (metric) depth from single RGB images, along with camera intrinsics and uncertainty. Designed to generalize across domains (zero-shot) using a self‑prompting camera module and pseudo-spherical prediction space. -
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OmniGen2
Multimodal generation AI model for image and text generation
OmniGen2 is a powerful, efficient open-source multimodal generation model designed for diverse AI tasks involving both images and text. It improves on its predecessor by introducing separate decoding pathways for text and image, along with unshared parameters and a decoupled image tokenizer, enhancing flexibility and performance. Built on a strong Qwen-VL-2.5 foundation, OmniGen2 excels in visual understanding, high-quality text-to-image generation, and instruction-guided image editing. It... -
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BLEURT-20-D12
Custom BLEURT model for evaluating text similarity using PyTorch
BLEURT-20-D12 is a PyTorch implementation of BLEURT, a model designed to assess the semantic similarity between two text sequences. It serves as an automatic evaluation metric for natural language generation tasks like summarization and translation. The model predicts a score indicating how similar a candidate sentence is to a reference sentence, with higher scores indicating greater semantic overlap. Unlike standard BLEURT models from TensorFlow, this version is built from a custom PyTorch... -
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vit-age-classifier
Vision Transformer model fine-tuned for facial age classification
vit-age-classifier is a Vision Transformer (ViT) model fine-tuned by nateraw to classify a person's age based on their facial image. Trained on the FairFace dataset, the model predicts age group categories using facial features with high accuracy. It leverages the robust image representation capabilities of ViT for fine-grained facial analysis. With 85.8 million parameters, the model operates efficiently for image classification tasks on faces. The model outputs probabilities for predefined... -
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table-transformer-detection
Transformer model for detecting tables in document images
table-transformer-detection is a fine-tuned DETR-based model by Microsoft for detecting tables in document images. Built on the Transformer architecture, it was trained on the PubTables1M dataset and excels at locating tabular structures in unstructured documents like PDFs. The model leverages the "normalize before" variant of DETR, applying layer normalization before attention layers. With 28.8 million parameters, it performs end-to-end object detection specific to tables without requiring... -
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chronos-bolt-base
Fast, accurate zero-shot time series forecasting with T5 encoder
chronos-bolt-base is a zero-shot time series forecasting model developed by the AutoGluon team, built on the T5-efficient-base architecture with 205 million parameters. It is part of the Chronos-Bolt family, trained on nearly 100 billion time series observations. The model transforms time series data into sequence patches, allowing the encoder to process historical context while the decoder directly generates quantile forecasts for multiple future steps. It significantly improves inference... -
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segmentation
Speaker segmentation model for voice activity and overlap detection
pyannote/segmentation is an advanced audio segmentation model designed for detecting speech activity, overlapping speech, and refining speaker diarization outputs. Built using pyannote.audio, it enables fine-grained, frame-level speaker segmentation from audio input. The model supports multiple pipelines such as Voice Activity Detection (VAD), Overlapped Speech Detection (OSD), and Resegmentation. It outputs either labeled time segments or raw probability scores indicating speech presence.... -
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roberta-base
Robust BERT-based model for English with improved MLM training
roberta-base is a robustly optimized variant of BERT, pretrained on a significantly larger corpus of English text using dynamic masked language modeling. Developed by Facebook AI, RoBERTa improves on BERT by removing the Next Sentence Prediction objective, using longer training, larger batches, and more data, including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. It captures contextual representations of language by masking 15% of input tokens and predicting them.... -
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colbertv2.0
Scalable BERT-based retrieval with late interaction for fast search
colbertv2.0 is a high-speed, high-accuracy retrieval model that enables scalable neural search over large text corpora using BERT-based embeddings. It introduces a “late interaction” mechanism where passages and queries are encoded into matrices of token-level embeddings. These are compared efficiently at search time using MaxSim operations, preserving contextual richness without sacrificing speed. Trained on datasets like MS MARCO, it significantly outperforms single-vector retrieval... -
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distilbert-base-uncased
Distilled version of BERT, optimized for speed and efficiency
distilbert-base-uncased is a compact, faster alternative to BERT developed through a distillation process. It retains 97% of BERT's language understanding performance while being 40% smaller and 60% faster. Trained on English Wikipedia and BookCorpus, it was distilled using BERT base as the teacher model with three objectives: distillation loss, masked language modeling (MLM), and cosine embedding loss. The model is uncased (treats "english" and "English" as the same) and is suitable for a... -
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bert-base-portuguese-cased
BERTimbau: BERT model pretrained for Brazilian Portuguese NLP
bert-base-portuguese-cased, also known as BERTimbau Base, is a BERT-based language model pretrained specifically for Brazilian Portuguese. Developed by NeuralMind, it has 12 layers and 110 million parameters. The model was trained using the brWaC corpus and achieves state-of-the-art performance in downstream tasks such as Named Entity Recognition, Sentence Textual Similarity, and Recognizing Textual Entailment. It supports case sensitivity (e.g., "Brasil" ≠ "brasil") and can be used for... -
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bert-base-multilingual-cased
Multilingual BERT model trained on 104 Wikipedia languages
bert-base-multilingual-cased is a multilingual version of BERT pre-trained on Wikipedia articles from the top 104 languages using masked language modeling (MLM) and next sentence prediction (NSP) objectives. Unlike uncased models, it preserves case distinctions (e.g., "english" ≠ "English"). Trained in a self-supervised fashion, this model captures deep bidirectional language representations, enabling it to be fine-tuned for a wide range of natural language understanding tasks across... -
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paraphrase-multilingual-MiniLM-L12-v2
Lightweight multilingual model for sentence similarity tasks
paraphrase-multilingual-MiniLM-L12-v2 is a compact sentence-transformers model that encodes sentences into 384-dimensional embeddings suitable for tasks such as semantic search, clustering, and paraphrase mining. Trained by the Sentence-Transformers team, it supports 50+ languages and builds on a distilled MiniLM architecture to balance speed and accuracy. The model uses mean pooling over token embeddings and is optimized for efficient inference, making it ideal for large-scale multilingual... -
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xlm-roberta-base
Multilingual RoBERTa trained on 100 languages for NLP tasks
xlm-roberta-base is a multilingual transformer model trained by Facebook AI on 2.5TB of filtered CommonCrawl data spanning 100 languages. It is based on the RoBERTa architecture and pre-trained using a masked language modeling (MLM) objective. Unlike models like GPT, which predict the next word, this model learns bidirectional context by predicting masked tokens, enabling robust sentence-level representations. xlm-roberta-base is particularly suited for cross-lingual understanding and... -
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wespeaker-voxceleb-resnet34-LM
Speaker embedding model for voice verification and identification
wespeaker-voxceleb-resnet34-LM is a pretrained speaker embedding model wrapped for use in pyannote.audio (v3.1+), built on the WeSpeaker toolkit and trained on the VoxCeleb dataset. It leverages a ResNet34 architecture and is designed for speaker recognition, verification, and diarization tasks. The model outputs dense embeddings from full audio, excerpts, or sliding windows, allowing for flexible speaker comparison using cosine similarity. Embeddings can be extracted easily with PyTorch and... -
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bge-large-en-v1.5
BGE-Large v1.5: High-accuracy English embedding model for retrieval
BAAI/bge-large-en-v1.5 is a powerful English sentence embedding model designed by the Beijing Academy of Artificial Intelligence to enhance retrieval-augmented language model systems. It uses a BERT-based architecture fine-tuned to produce high-quality dense vector representations optimized for sentence similarity, search, and retrieval. This model is part of the BGE (BAAI General Embedding) family and delivers improved similarity distribution and state-of-the-art results on the MTEB benchmark... -
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bge-small-en-v1.5
Compact English sentence embedding model for semantic search tasks
BAAI/bge-small-en-v1.5 is a lightweight English sentence embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI) as part of the BGE (BAAI General Embedding) series. Designed for dense retrieval, semantic search, and similarity tasks, it produces 384-dimensional embeddings that can be used to compare and rank sentences or passages. This version (v1.5) improves similarity distribution, enhancing performance without the need for special query instructions. The model... -
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ERNIE-4.5-VL-424B-A47B-Base-Paddle
Latent diffusion model generating high-quality text-to-image outputs
ERNIE-4.5-VL-424B-A47B-Base-Paddle is a multimodal Mixture-of-Experts (MoE) model developed by Baidu, designed to understand and generate both text and image-based information. It utilizes a heterogeneous MoE architecture with modality-isolated routing and specialized loss functions to ensure effective learning across both modalities. Pretrained with trillions of tokens, the model activates 47B parameters per token out of a total of 424B, optimizing for scalability and precision. Its... -
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Nanonets-OCR-s
State-of-the-art image-to-markdown OCR model
Nanonets-OCR-s is an advanced image-to-markdown OCR model that transforms documents into structured and semantically rich markdown. It goes beyond basic text extraction by intelligently recognizing content types and applying meaningful tags, making the output ideal for Large Language Models (LLMs) and automated workflows. The model expertly converts mathematical equations into LaTeX syntax, distinguishing between inline and display modes for accuracy. It also generates descriptive <img> tags... -
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FLUX.1-dev
Powerful 12B parameter model for top-tier text-to-image creation
FLUX.1-dev is a powerful 12-billion parameter rectified flow transformer designed for generating high-quality images from text prompts. It delivers cutting-edge output quality, just slightly below the flagship FLUX.1 [pro] model, and matches or exceeds many closed-source competitors in prompt adherence. The model is trained using guidance distillation, making it more efficient and accessible for developers and artists alike. FLUX.1-dev is openly available with weights provided to support... -
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stable-diffusion-v1-4
Text-to-image diffusion model for high-quality image generation
stable-diffusion-v1-4 is a high-performance text-to-image latent diffusion model developed by CompVis. It generates photo-realistic images from natural language prompts using a pretrained CLIP ViT-L/14 text encoder and a UNet-based denoising architecture. This version builds on v1-2, fine-tuned over 225,000 steps at 512×512 resolution on the “laion-aesthetics v2 5+” dataset, with 10% text-conditioning dropout for improved classifier-free guidance. It is optimized for use with Hugging Face’s...
