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.
Brief Details: DistilBERT base uncased - Lightweight BERT variant (67M params), trained on BookCorpus & Wikipedia. Fast, efficient language model for NLP tasks.
Brief-details: RoBERTa-large: 355M parameter transformer model by Facebook AI, trained on 160GB text data for masked language modeling, achieving SOTA results on GLUE benchmarks.
Brief-details: Powerful multilingual speech recognition model with 1.54B parameters, supporting 99 languages. Trained on 680k hours of audio data for transcription and translation.
Brief-details: Vision Transformer (ViT) model with 86.4M parameters, trained on ImageNet-21k dataset for image recognition tasks. Supports PyTorch and JAX frameworks.
Brief-details: A PyTorch-based speaker recognition model using ResNet34 architecture, trained on VoxCeleb dataset for voice embedding and speaker verification tasks. Popular with 13M+ downloads.
Brief Details: OPT-1.3B is Meta AI's open-source language model with 1.3B parameters, trained on 180B tokens for text generation and language understanding.
Brief Details: CLIP-based vision transformer model with large architecture (patch size 14, 336px images) for zero-shot image classification and multimodal tasks
Brief-details: RoBERTa base - 125M parameter transformer model by FacebookAI. Pretrained on 160GB text data using masked language modeling. Popular for NLP tasks.