Gemma-UA-Cardio consists of two professional large language models specifically designed for the Ukrainian cardiology field. They are based on Google's Gemma and MedGemma models and have undergone two-stage fine-tuning (language adaptation and domain specialization). They can accurately provide cardiovascular medical information and answer clinical questions in Ukrainian, serving as auxiliary tools for medical professionals.

Natural Language ProcessingTransformersOther

Gemma 3n E4B IT is a lightweight multimodal open model launched by Google, built based on the same research as the Gemini model. This model supports text, audio, and visual inputs and is suitable for various tasks. It uses the MatFormer architecture to achieve efficient parameter utilization.

MultimodalTransformers

Gemma 3 is a lightweight open-source multimodal model launched by Google. This version is an instruction-tuned quantization-aware training model with 12B parameters, which has been converted to the MXFP4 format of the MLX framework. It supports text and image input and generates text output, with a 128K context window and support for over 140 languages.

MultimodalMlx

This is a housing condition classifier fine-tuned based on the Google ViT-base model, which can classify housing images into four categories: good, unknown, dilapidated, and medium. The model was trained on a dataset of 935 housing images, and the accuracy on the validation set reached 81.2%.

Computer VisionTransformersEnglish

This is a Dutch patient simulation model fine-tuned based on Google's Gemma 2 9B model, specifically designed for medical education scenarios. The model always responds in a fixed JSON structure, simulating a patient 'Maria' who has just completed surgery and is in the recovery process, supporting nursing students to exercise communication and clinical reasoning skills in a safe environment.

Natural Language ProcessingGgufOther

This is an English-Spanish translation model fine-tuned on the Amazon dataset based on the Google mT5-small model, specifically designed for text abstract generation tasks. The model achieved scores of Rouge1: 16.44 and Rouge2: 8.04 on the evaluation set.

Natural Language ProcessingTransformers

FLAN-T5 Small is an instruction fine-tuned text generation model developed by Google, based on the small version of the T5 architecture. This model has undergone instruction fine-tuning on various NLP tasks and can understand and execute various text generation instructions.

Natural Language ProcessingSafetensorsEnglish

This is a text generation model fine-tuned based on the Google Gemma-3-4B-IT model, specifically designed to generate diverse and relevant search queries according to the metadata of demo templates. This model uses the LoRA adapter for efficient fine-tuning and is a key component of the structured document field adaptive dense retrieval framework.

Natural Language ProcessingSafetensorsEnglish

This is a text generation model optimized using PEFT (Parameter-Efficient Fine-Tuning) and LoRA techniques based on the Google Gemma-2B-IT base model. The model is optimized for specific tasks while maintaining the capabilities of the base model through parameter-efficient methods.

Natural Language ProcessingPeft

These are various GGUF format quantized versions of the Google embeddinggemma-300m base model, optimized for efficient deployment and use in different scenarios. This model focuses on sentence embedding and similarity calculation tasks.

Natural Language ProcessingGgufEnglish

Scout is a professional language model fine-tuned by VANTA Research based on the Google Gemma 3 4B Instruct architecture. It excels in constraint-aware reasoning and adaptive problem solving, and is擅长侦察式情报收集 should be translated as is good at scouting-style intelligence gathering。擅长侦察式情报收集、系统问题分解和自适应解决方案生成。It is good at scouting-style intelligence gathering, system problem decomposition, and adaptive solution generation.

Natural Language ProcessingTransformersEnglish

The MLX MXFP4 quantized version of Gemma 3 27B IT QAT is a lightweight open - source multimodal model developed by Google. This model can simultaneously process text and image inputs and generate text outputs. It has a large context window of 128K and supports over 140 languages, making it suitable for various text generation and image understanding tasks.

MultimodalMlx

This is a text generation model that is specifically fine-tuned based on the Google Gemma-3 model. After training, it can use external tools to generate step-by-step plans. This model is trained on the rewoo/planner_instruction_tuning_2k dataset and uses LoRA technology for efficient fine-tuning.

Natural Language ProcessingTransformers

This is a lightweight text summary generation model based on the Google T5 Efficient Tiny architecture. It is trained using the shorecode/summary-collection-200k-rows dataset and is specifically designed for automatic text summarization tasks. It features efficient inference and low resource consumption.

Natural Language ProcessingTransformersEnglish

A version fine-tuned based on google/medgemma-4b-it, specifically designed for binary breast X-ray image classification on the OMAMA 256×256 dataset. This model uses LoRA (Low-Rank Adaptation) for parameter-efficient adjustment and performs excellently in the breast cancer classification task, with the accuracy increasing from 12% of the base model to 95%.

MultimodalTransformersEnglish

Lapa LLM 12B PT is an open-source large language model developed based on Google Gemma 3-12B, specifically optimized for Ukrainian language processing. It is developed by research teams from multiple Ukrainian universities and performs excellently in Ukrainian language processing, with efficient instruction fine-tuning and multimodal capabilities.

MultimodalTransformersOther

VaultGemma is a lightweight language model pre-trained by Google based on differential privacy technology, with powerful privacy protection capabilities. It uses differential privacy stochastic gradient descent (DP-SGD) for pre-training, providing mathematical privacy guarantees for the training data. It has less than 1 billion parameters and is suitable for a variety of natural language processing tasks.

Natural Language ProcessingTransformers

This is a text embedding model converted to the MLX format, derived from Google's EmbeddingGemma-300m model. It is specifically designed for sentence similarity calculation and text embedding tasks. The model uses BF16 precision and is suitable for efficient inference on Apple chip devices.

Natural Language ProcessingSentence-transformers

EmbeddingGemma 300M 4bit is a lightweight text embedding model developed by Google, specifically optimized for the MLX framework. This model can convert text into high-quality vector representations and is suitable for various natural language processing tasks, especially sentence similarity calculation and feature extraction.

Natural Language ProcessingSentence-transformers

embeddinggemma-300m-qat-q8_0 GGUF is a quantized version based on the Google embeddinggemma-300m model, specifically designed for feature extraction and sentence similarity calculation. This model has been quantized and optimized to significantly reduce the model size and inference overhead while maintaining high accuracy.

Natural Language ProcessingSentence-transformers