Brief-details: CLIP (Contrastive Language-Image Pre-training) by OpenAI - A powerful vision-language model using ViT-B/16 architecture for zero-shot image classification with 20M+ downloads.
Brief Details: A powerful English speech recognition model with 315M parameters, fine-tuned from XLSR-53. Achieves 19.06% WER on Common Voice, optimized for 16kHz audio.
Brief Details: Qwen2.5-1.5B-Instruct is a 1.54B parameter instruction-tuned LLM with 32K context length, supporting 29+ languages and optimized for chat applications
BRIEF DETAILS: OpenAI's CLIP-ViT-Large visual transformer model with 428M parameters for zero-shot image classification, featuring dual image-text encoders and extensive dataset training.
Brief Details: Efficient sentence embedding model with 22.7M parameters, maps text to 384D vectors. Trained on 1B+ sentence pairs, ideal for similarity tasks.
Brief-details: BERT base uncased (110M params) - Foundational transformer model for English language tasks with masked language modeling, trained on BookCorpus and Wikipedia.
Brief-details: XLM-RoBERTa large: Multilingual transformer model with 561M parameters, trained on 2.5TB CommonCrawl data covering 100 languages. Optimized for masked language modeling and cross-lingual tasks.
Brief-details: Powerful sentence embedding model with 109M params, trained on 1B+ sentence pairs. Maps text to 768D vectors for semantic search & similarity tasks.
BRIEF-DETAILS: CLIP-ViT model for zero-shot image classification, using Vision Transformer architecture. 23M+ downloads, created by OpenAI for research purposes.
Brief-details: A powerful 14B parameter code-generation model from Qwen with 128K context length, optimized for programming, code reasoning, and fixing. Built on Qwen2.5 architecture.
Brief Details: A specialized BERT-large model trained on 256GB of legal text, optimized for legal NLP tasks with 32k vocabulary including legal terms
BRIEF-DETAILS: QwQ-32B-Preview: A 32.8B parameter experimental research model focused on advanced reasoning, featuring 32K context length and specialized architecture with RoPE and SwiGLU.
Brief-details: A highly saturated VAE-integrated anime-style image generation model with multiple versions (v1-v3), optimized for cute character aesthetics and detailed clothing expressions.
Brief Details: A specialized LoRA model for FLUX.1-dev that generates children's simple sketches with pastel colors, trained for creating innocent, sketch-style artwork.
Brief-details: Quantized version of Gemma-2-9B with multiple GGUF variants optimized for different hardware setups. Features 9.24B parameters with comprehensive quantization options from 3.43GB to 36.97GB.
Brief Details: Advanced image captioning model based on Florence-2, optimized for AI art prompting. Features 823M params, multiple caption styles, and low VRAM usage.
BRIEF DETAILS: Specialized 8B parameter healthcare LLM built on Llama 3, achieving SOTA results for its size. Features advanced medical QA capabilities and ethical safeguards.
Brief-details: A 12.9B parameter MoE model based on Mixtral architecture, optimized with DPO and laser techniques. Strong performance on reasoning tasks with 67.16% avg on OpenLLM.
Brief-details: A powerful 47B parameter Traditional Chinese-focused instruction-tuned LLM built on Mixtral-8x7B, featuring expanded vocabulary and achieving GPT-3.5-turbo level performance.
Brief-details: A 7B parameter Mamba architecture model trained on RefinedWeb dataset, featuring linear-time sequence modeling and strong performance across NLP tasks
Brief-details: Advanced multimodal AI model combining Mistral-7B with CLIP-ViT-L for image understanding, featuring dual-encoder architecture and Russian language support.
