BRIEF DETAILS: Chronos-T5-Small: A 46M parameter time series forecasting model based on T5 architecture. Enables probabilistic forecasts through token-based sequence modeling.
BRIEF DETAILS: BERT multilingual model supporting 104 languages with 179M parameters. Pre-trained on Wikipedia data using masked language modeling. Apache 2.0 licensed.
Brief-details: A BERT-like transformer model optimized for long documents up to 4,096 tokens, featuring sliding window attention and global attention mechanisms.
Brief Details: OPT-125M is Meta AI's smallest open-source GPT-style language model with 125M parameters, designed for text generation and research accessibility.
Brief-details: Efficient sentence embedding model with 22.7M params, maps text to 384D vectors. Popular choice with 5.9M+ downloads. Apache 2.0 licensed.
Brief Details: ALBERT Base v2 - Lightweight BERT variant with 11.8M params, sharing layer weights. Trained on BookCorpus & Wikipedia for MLM tasks.
Brief-details: T5-small is a compact 60M parameter text-to-text transformer model that can handle multiple NLP tasks like translation, summarization and QA through a unified text-based approach.
Brief Details: Efficient sentence embedding model with 33.4M params, trained on 1B+ sentence pairs. Maps text to 384D vectors for similarity tasks.
Brief Details: BERT base cased model (109M params) - Pretrained transformer for masked language modeling and next sentence prediction on English text.
Brief-details: DINOv2 base model - Self-supervised Vision Transformer for robust visual feature extraction. 86.6M params, Apache 2.0 licensed.
Brief-details: ELECTRA base discriminator model from Google - pre-trained transformer that learns by detecting real vs fake tokens, with 9.2M+ downloads and Apache 2.0 license
Brief-details: Supervised SimCSE model built on RoBERTa-large, specialized in sentence embeddings and feature extraction. Trained on MNLI/SNLI datasets for enhanced semantic similarity tasks.
Brief-details: DistilBERT model fine-tuned on SST-2 dataset for sentiment classification, achieving 91% accuracy. Lightweight with 67M parameters, ideal for production deployment.
BRIEF-DETAILS: 8B parameter LLaMA 3.1 model optimized for instruction-following, available in multiple GGUF quantizations for efficient deployment on various hardware configurations.
Brief-details: XLM-RoBERTa base model: Multilingual transformer with 279M parameters, trained on 2.5TB CommonCrawl data covering 94 languages. Specializes in masked language modeling and cross-lingual tasks.
Brief Details: Multilingual sentence embedding model supporting 50+ languages, maps text to 384D vectors, 118M parameters, ideal for semantic search & clustering.
Brief-details: Whisper-small is a 244M parameter speech recognition model trained on 680k hours of data, supporting 99 languages with strong transcription and translation capabilities.
Brief-details: Multilingual CLIP model extending OpenAI's vision-language capabilities to 48 languages, using XLM-RoBERTa architecture with ViT-B/32 visual backbone. Popular with 12M+ downloads.
Brief-details: A 560M parameter multilingual language model fine-tuned on xP3 dataset, capable of following instructions in 46 languages with strong zero-shot learning abilities.
Brief-details: ResNet-50 A1 model with 25.6M params, trained on ImageNet-1k using LAMB optimizer and cosine LR schedule. Achieves 81.22% top-1 accuracy.
Brief-details: GPT-2 (124M params) - OpenAI's transformer-based language model for text generation. Popular base model with 17M+ downloads. MIT licensed.
