# Zero-shot Learning

Magma-8B is an image-text-to-text conversion model based on the GGUF format, suitable for multimodal task processing.

Transformers is an open-source library developed by Hugging Face, providing various pretrained models for natural language processing tasks.

OpenVision is a fully open, cost-effective family of advanced vision encoders focused on multimodal learning.

This model is based on the transformers library, and its specific purpose and functionality require further information to determine.

Falcon-H1 is an efficient hybrid architecture language model developed by TII, combining Transformer and Mamba architectures to support multilingual tasks

OpenBioNER is a lightweight BERT model specifically designed for open-domain biomedical named entity recognition (NER). It can identify unseen entity types using only natural language descriptions of target entity types, without requiring retraining.

XGLM-564M is a multilingual autoregressive language model with 564 million parameters, trained on a balanced corpus of 30 languages totaling 500 billion subwords.

Zero-Mistral-24B is an improved text-only model based on Mistral-Small-3.1-24B-Instruct-2503, primarily adapted for Russian and English, with the original visual capabilities removed to focus on text generation tasks.

This is a transformers model hosted on Hugging Face Hub. Specific functions and uses require further information.

A multilingual text classification model fine-tuned based on xlm-roberta-large, used for minor topic code classification in comparative agenda projects

This is an improved version of the Facebook SAM model (sam-vit-base), specifically optimized for image segmentation tasks in CVAT.

Quantum_STT is an advanced automatic speech recognition (ASR) and speech translation model, trained with large-scale weak supervision, supporting multiple languages and tasks.

Kok-Base is a multilingual model supporting English, Arabic, and Czech, suitable for various natural language processing tasks.

HiMTok is a hierarchical mask token learning framework fine-tuned on the InternVL2_5-8B large multimodal model, focusing on image segmentation tasks.

Distill-Any-Depth is a SOTA monocular depth estimation model trained based on knowledge distillation algorithms, capable of efficient and accurate depth estimation.

An anime-style text-to-image generation model based on Stable Diffusion XL, focusing on high-quality anime character creation

olmOCR-7B-0225-preview is an image-to-text model based on OCR technology developed by AllenAI, designed to extract and recognize text content from images.

LLaVA-NeXT-Video-7B-hf is a video-based multimodal model capable of processing video and text inputs to generate text outputs.

A 7B-parameter language model based on the Qwen2.5 architecture, created by fusing multiple pre-trained models using the TIES method

Vision Transformer model based on SigLIP 2, designed for image feature extraction, pre-trained on the webli dataset

Vision Transformer model based on SigLIP 2, designed for image feature extraction, trained on the webli dataset

A vision Transformer model based on the SigLIP 2 architecture, featuring a Global Average Pooling (GAP) variant that removes the attention pooling head, suitable for image feature extraction tasks.

ViT image encoder based on SigLIP 2, designed for timm, suitable for vision-language tasks

SigLIP 2 ViT image encoder, using global average pooling, with attention pooling head removed, designed specifically for timm

Vision Transformer model based on SigLIP 2 technology, focused on image feature extraction

Vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction

A ViT image encoder based on SigLIP 2, using global average pooling with the attention pooling head removed, suitable for image feature extraction tasks.

Vision Transformer model based on SigLIP 2, designed for image feature extraction, pre-trained on the webli dataset

A ViT image encoder based on SigLIP 2, utilizing global average pooling, suitable for vision-language tasks.

This is a quantized version based on the Qwen2.5-14B-Instruct model, specifically designed for citation intent classification tasks.

A citation intent classification model fine-tuned based on Qwen2.5-14B-Instruct, specializing in citation analysis tasks in scientific literature.

GLiNER-BioMed is an efficient open NER model suite based on the GLiNER framework, specifically designed for the biomedical domain to recognize various types of biomedical entities.

GLiNER-BioMed is an efficient open biomedical named entity recognition model suite based on the GLiNER framework, specifically designed for the biomedical domain, capable of recognizing multiple entity types.

HealthGPT is a model specifically developed for unified multimodal medical tasks.

Cuckoo is a small (300M parameters) information extraction model that efficiently extracts information by mimicking the next-word prediction paradigm of large language models

ENEL is a model exploring the potential of encoder-free architecture in 3D large multimodal models.

Pi0 is a general robot control model based on vision-language-action flow, supporting robot control tasks.

This model is used to classify audio clips as either 'Suno' music or 'People' music.

InternLM3-8B-Instruct is an 8-billion-parameter instruction model developed by Shanghai AI Laboratory, designed for general-purpose use and advanced reasoning, featuring high efficiency and low cost.

SAM2 model based on the HieraDet image encoder, focused on image feature extraction tasks.