# Few-shot Learning

LWM 1.1 is an upgraded pre-trained model specifically designed for wireless channel feature extraction, supporting diverse channel configurations to enhance feature extraction quality and generalization capabilities.

A fine-tuned vision-language model based on Salesforce/blip2-opt-2.7b for visual question answering tasks

A Vision Transformer model fine-tuned on the beans dataset based on google/vit-base-patch16-224-in21k for image classification tasks

Video classification model fine-tuned on a subset of UCF101 based on the VideoMAE base model

A dialogue model fine-tuned based on Qwen 2.5 7B Instruct model, trained with Gamma Corpus v2-100k

This is an unconditional image generation model based on diffusion models, specifically designed for generating cute butterfly images.

An audio classification model fine-tuned on the minds14 dataset based on facebook/wav2vec2-base

A food image segmentation model based on ConvNeXt architecture, fine-tuned on the FoodSeg103 dataset, specifically designed to recognize and segment different ingredient categories in food images.

A multimodal query rewrite model fine-tuned based on google/paligemma-3b-pt-224

LWM is the first foundational model in the field of wireless communications, developed as a universal feature extractor capable of extracting fine-grained representations from wireless channel data.

A diffusion model-based aurora image generation model capable of producing high-quality aurora images.

This is a text classification model based on the SetFit framework, specifically designed for contract clause classification tasks with an accuracy of 94.25%.

A text classification model trained with AutoTrain, suitable for few-shot learning scenarios, capable of efficiently classifying news articles.

Rho-1 is a language model specialized in mathematics, pretrained with Selective Language Modeling (SLM) method, significantly improving accuracy in solving mathematical problems.

A Bhutanese textile image classification model fine-tuned based on Google's ViT model

A dialogue AI based on Causal Language Modeling (CLM) architecture, designed for natural language interaction, capable of generating coherent and contextually appropriate responses.

A 7B-parameter large language model based on the Mistral architecture, efficiently trained using Unsloth and TRL libraries, demonstrating excellent performance in multiple benchmarks.

Vision Transformer (ViT)-based skin cancer image classification model capable of identifying 7 types of skin lesions

A fine-tuned Alzheimer's disease MRI image classification model based on Google's ViT base model, achieving 92.6% accuracy

BoreanGale-70B is a 70B-parameter large language model merged using a custom algorithm (NearSwap), combining the miqu-1-70b-sf and WinterGoddess-1.4x-70B-L2 models.

StrangeMerges_17-7B-dare_ties is the result of merging two models, Gille/StrangeMerges_16-7B-slerp and Gille/StrangeMerges_12-7B-slerp, using the dare_ties merging method via LazyMergekit.

DareBeagle-7B is a 7B-parameter large language model obtained by merging mlabonne/NeuralBeagle14-7B and mlabonne/NeuralDaredevil-7B using LazyMergekit, demonstrating excellent performance across multiple benchmarks.

A text classification model fine-tuned based on bert-tiny, demonstrating good precision and accuracy on the evaluation set

A 6.7 billion parameter large language model based on the Galactica architecture, specializing in essay writing tasks, with an average score of 37.75 on the Open LLM Leaderboard.

Misted-7B is a 7B-parameter large language model based on the fusion of OpenHermes-2-Mistral-7B and Mistral-7B-SlimOrca, primarily designed for text generation tasks.

This model is a fine-tuned version of MCG-NJU/videomae-base on a traffic accident video dataset, primarily designed for video classification tasks.

A biology domain model fine-tuned based on GPT-2, with unspecified training data and purposes

A few-shot learning text classification model based on the SetFit framework, achieving efficient classification through contrastive learning and classification head training

Image classification model fine-tuned based on google/vit-base-patch16-224-in21k, suitable for plant classification tasks

This model is a vision-language model based on the ViLT architecture, fine-tuned on VQA datasets, suitable for visual question answering tasks.

orca_mini_13b is a text generation model trained on multiple high-quality datasets, focusing on instruction following and dialogue tasks.

A sentence transformer model initialized from sentence-transformers/all-mpnet-base-v2, specifically designed for few-shot contextual example retrieval in the MultiWOZ dataset

A sentence transformer model initialized from sentence-transformers/all-mpnet-base-v2, specifically designed for few-shot context example retrieval in the MultiWOZ dataset

This model is a speech classification model fine-tuned on the Minds14 dataset based on the WavLM pre-trained model, primarily used for speech intent recognition tasks.

A model for gender classification based on text or image data, with unspecified training data.

A high-precision image classification model fine-tuned on the beans dataset based on Google's ViT model

A Vision Transformer model fine-tuned on a flower image dataset based on Google's ViT-Base-Patch16-224 model

A vision Transformer model fine-tuned on a flower image dataset based on Google's ViT model

An image classification model fine-tuned based on google/vit-base-patch16-224, suitable for histology image analysis tasks.

A vision model fine-tuned based on google/vit-base-patch16-224, suitable for image classification tasks