# Image caption generation

Gemma 3 4B IT QAT 4bit is a 4-bit quantized large language model trained with Quantization-Aware Training (QAT), based on the Gemma 3 architecture and optimized for the MLX framework.

A GPT4-O style caption generator fine-tuned based on Florence-2-base-ft for generating image descriptions

4-bit quantized version based on meta-llama/Llama-3.2-11B-Vision-Instruct, supporting image understanding and text generation tasks

This is a LORA fine-tuned version of the Qwen2-VL-2B model for Russian, supporting multimodal tasks.

BLIP is a unified vision-language pretraining framework, excelling in tasks like image caption generation and visual question answering, with performance enhanced by innovative data filtering mechanisms

Florence-2 is an advanced vision foundation model developed by Microsoft, employing a prompt-based approach to handle various vision and vision-language tasks.

An image caption generation model fine-tuned on the DocCI dataset based on PaliGemma-3b, capable of generating detailed descriptions of 200-350 characters with reduced hallucination

An image-to-text model based on the Transformers library, capable of converting image content into descriptive text, particularly suitable for the art domain.

UForm-Gen is a small generative vision-language model primarily used for image caption generation and visual question answering.

A vision-language model fine-tuned from Microsoft's git-base model, specifically designed to generate descriptive text captions for images from the anime 'One Piece'