# Image Caption Generation

This is a multimodal large language model quantized with A8W8, based on Qwen2.5-VL-7B-Instruct, supporting vision and language tasks.

Vision encoder based on Mistral Small model, supports image-text generation tasks, compatible with llama.cpp framework

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

BLIP is a unified vision-language pretraining framework, excelling in vision-language tasks such as image caption generation

This is an MLX-format model converted from the Google Gemma 3-12B model, supporting image-text-to-text tasks.

GIT is a Transformer-based image-to-text generation model capable of generating descriptive text from input images.

This model is a GGUF format conversion based on Qwen2-VL-7B-Captioner-Relaxed, optimized for image-to-text tasks and supports running via tools like llama.cpp and Koboldcpp.

This is an FP8 compressed version of the Llama JoyCaption Alpha Two model developed by fancyfeast, implemented using the llm-compressor tool and compatible with the vllm framework.

A vision-language model pre-trained on the COCO dataset, excelling in generating accurate image descriptions

An image caption generation model fine-tuned based on microsoft/Florence-2-base, focusing on improving description quality and format

The 4Bit quantized version of Molmo-7B-D-0924, which reduces VRAM usage through the NF4 quantization strategy and is suitable for environments with limited VRAM.

LLaVA-Llama3 is a multimodal model based on Llama-3, supporting joint processing of images and text.

This is a GGUF format model converted from the Qwen2-VL-7B-Captioner-Relaxed model, specifically designed for image-to-text tasks.

ViTucano is a natively Portuguese pre-trained visual assistant that integrates visual understanding and language capabilities, suitable for multimodal tasks.

GIT is a Transformer-based generative image-to-text model capable of converting visual content into textual descriptions.

BLIP is a Transformer-based image captioning model capable of generating natural language descriptions for input images.

An image captioning model based on the ViT-GPT2 architecture, capable of generating natural language descriptions for input images.

An image caption generation model based on the ViT-GPT2 architecture, capable of converting input images into descriptive text

A customized small vision model based on Moondream2, fine-tuned specifically for image caption generation tasks

This model is used to convert image content into textual descriptions and is suitable for non-commercial purposes.

The Peacock Model is an Arabic multimodal large language model based on the InstructBLIP architecture, with AraLLaMA as its language model.

A Japanese vision-language model based on Llama-3-8B-Instruct, enhanced with additional pretraining and instruction tuning for improved Japanese capabilities

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

Florence-2-SD3-Captioner is an image caption generation model based on the Florence-2 architecture, specifically designed for generating high-quality image captions.

distilvit is an image-to-text model based on a VIT image encoder and a distilled GPT-2 text decoder, capable of generating textual descriptions of images.

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

DistilViT is an image caption generation model based on Vision Transformer (ViT) and distilled GPT-2, capable of converting images into textual descriptions.

ConvLLaVA-JP is a Japanese vision-language model that supports high-resolution input and can engage in conversations about input images.

This model can convert input images into descriptive text, suitable for image understanding tasks in various scenarios.

This model is an image-to-text model based on the Apache-2.0 license, capable of converting image content into textual descriptions.

PaliGemma is a multi-functional lightweight vision-language model that combines image and text inputs to generate text outputs and supports multiple languages.

PaliGemma is a lightweight vision-language model that combines image and text inputs to generate text outputs, supporting multilingual and multi-task processing.

PaliGemma is a multi-functional lightweight vision-language model that supports image and text input and generates text output, suitable for various vision-language tasks.

This model is a quantized version of Salesforce/blip-image-captioning-base, reducing floating-point precision to bfloat16, cutting memory usage by 50%, and is suitable for image-to-text generation tasks.

This is a transformers-based image-to-text conversion model, specific functionalities require further details

A vision-language model capable of conversing about input images, supporting Japanese interaction

UForm-Gen2-dpo is a small generative vision-language model, aligned for image caption generation and visual question answering tasks through Direct Preference Optimization (DPO) on VLFeedback and LLaVA-Human-Preference-10K preference datasets.

A fine-tuned version of Moondream2, optimized for image prompt generation. It is a lightweight vision-language model suitable for efficient operation on edge devices.

A vision-language model based on VIT image encoder and distilled GPT-2 text decoder for image caption generation tasks

This is a vision-based image-to-text model capable of generating image descriptions.