# Quantized Efficient Inference

Medra4b is a multilingual (English and Romanian) text generation model fine-tuned on medical datasets, specializing in medical domain applications.

Medra4b is a quantized language model specialized in the medical field, supporting English and Romanian, with a focus on text generation and summarization tasks.

A statically quantized model based on the T5 architecture, specialized for academic paper abstract generation tasks

Clinician-Note-2.0a is a text generation model specialized in the medical field, particularly suitable for AI-assisted clinical documentation and summarization.

Medra is a quantized language model fine-tuned on medical datasets, specializing in medical Q&A and summarization tasks.

Gemma 3 is a lightweight, cutting-edge open model series from Google, built on the same technology as Gemini, supporting multimodal input (text/image) and text output. The 27B version significantly reduces memory requirements through quantization-aware training.

Gemma 3 is Google's lightweight open model series based on Gemini technology, supporting multimodal input and text output.

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

This is a multimodal model based on the Qwen2 architecture, supporting text generation, image-to-text, and visual question answering tasks, with multiple quantized versions to meet diverse needs.

Qwen2-VL-7B-Instruct is a multimodal vision-language model that supports joint understanding and generation tasks for images and text.