Trillion-LLaVA-7B-FP16 Open-Source Vision-Language Model - Free Deployment for Excellent Image Understanding and Cross-Language Inference

Trillion LLaVA 7B FP16

Developed by trillionlabs

Trillion-LLaVA-7B is a vision-language model with image understanding capabilities, trained on English visual-language instruction pairs, demonstrating exceptional cross-lingual visual reasoning abilities.

Text-to-Image

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Multilingual Visual Reasoning #Zero-shot Cross-lingual Transfer #English-Korean Bimodal

Downloads 14

Release Time : 4/20/2025

Model Overview

This model is developed based on Trillion-7B-preview, adopting the same architecture and training strategy as LLaVA, focusing on vision-language understanding tasks, particularly showcasing outstanding performance in Korean visual reasoning tasks.

Model Features

Cross-lingual Visual Reasoning Ability

Trained only with English visual-language pairs, yet performs excellently in Korean visual reasoning tasks

Two-stage Training Strategy

Adopts the same two-stage training method as LLaVA to ensure model performance

Multilingual Foundation

Strong multilingual capabilities enable effective cross-lingual visual reasoning transfer

Model Capabilities

Image Understanding

Visual Question Answering

Cross-lingual Visual Reasoning

Multimodal Understanding

Use Cases

Visual Question Answering Systems

Multilingual Visual Question Answering

Supports answering image-related questions in English and Korean

Achieved a score of 0.61 in the MMBENCH Korean test

Educational Assistance

Multilingual Learning Aid

Helps learners understand different languages through visual content

🚀 Trillion-LLaVA-7B

A Vision Language Model (VLM) capable of understanding images, demonstrating strong performance in multilingual visual reasoning.

🚀 Quick Start

This section is not provided in the original README, so it is skipped.

✨ Features

Multilingual Visual Reasoning: Despite being trained mainly on English vision - language instruction pairs, the model shows strong performance on Korean visual reasoning tasks, indicating effective transfer of visual reasoning capabilities across languages.
Same Strategy as LLaVA: Adopted the same dataset, two - stage training strategy, and model architecture as LLaVA for better observation of multilinguality transfer in vision tasks.

📚 Documentation

Introduction

We introduce Trillion-LLaVA-7B, a Vision Language Model (VLM) capable of understanding images.

To better observe the transfer of multilinguality in vision tasks under controlled conditions, we adopted the same dataset, two - stage training strategy, and model architecture as LLaVA. While Trillion-7B-preview-vision was trained exclusively on English vision - language instruction pairs, the model is able to demonstrate strong performance on Korean visual reasoning tasks. The results indicate that our model’s robust multilingual foundation enables the effective transfer of visual reasoning capabilities across languages without requiring language - specific visual training data.

Evaluation

Performance comparison (English, Korean) across different vision - language models

Model	MMBENCH En	MMBENCH Ko	SEED - I En	SEED - I Ko	MMStar En	MMStar Ko	K - DTCB
Llava - 1.5 - 7b	0.64	0.43	0.66	0.52	0.34	0.33	0.30
Llava - 1.6 - mistral - 7b	0.68	0.49	0.72	0.61	0.36	0.33	0.30
Trillion - LLaVA - 7B	0.66	0.61	0.68	0.66	0.37	0.37	0.33

Limitations

Lack of multilingual visual training: The model was trained exclusively on English vision - language pairs, which leaves room for improvement on other language pairs.
Inherited limitations: The model inherits the limitations of Trillion - 7B - preview, since no additional training was done except on vision language understanding data.

📄 License

This model repository is licensed under the Apache - 2.0 License.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご