Model Card for InternVL3Fangwusha8B

InternVL3Fangwusha8B is an 8B-parameter vision-language model (VLM) fine-tuned from InternVL3-8B, optimized for high-performance Chinese multimodal understanding, complex visual reasoning, document analysis, table extraction, and image-text dialogue in industrial and advanced application scenarios.

Model Details

Model Description

This model is a mid-to-large scale vision-language model based on the InternVL3-8B foundation architecture. It is fine-tuned to enhance cross-modal alignment, complex image understanding, structured information extraction from documents, and multi-turn visual dialogue in Chinese. It provides strong reasoning ability while maintaining efficient deployability.

• Developed by: Yougen Yuan
• Funded by [optional]: Personal Research Project
• Shared by [optional]: Yougen Yuan
• Model type: Vision-Language Model (VLM), Multimodal Large Language Model
• Language(s) (NLP): Chinese (Simplified)
• License: Apache-2.0
• Finetuned from model [optional]: InternVL3-8B

Model Sources [optional]

• Repository: https://huggingface.co/Yougen/InternVL3Fangwusha8B
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be directly used for:

• Chinese visual question answering (VQA) in complex scenes
• High-quality image captioning and detailed visual description
• Document analysis, table recognition, form understanding and key information extraction
• Multi-turn image-text dialogue and interactive reasoning
• OCR + semantic understanding for scanned documents and photos

Downstream Use [optional]

Can be further fine-tuned for:

• Enterprise-level intelligent document processing systems
• Educational and professional visual question answering applications
• E-commerce product image understanding and content generation
• Multimodal RAG systems with visual information retrieval
• AI assistants with image understanding capabilities

Out-of-Scope Use

• Not intended for unregulated high-stakes visual tasks (medical imaging, autonomous driving, industrial defect detection without professional certification)
• Not suitable for generating harmful, illegal, pornographic, violent or privacy-violating multimodal content
• Not optimized for non-Chinese languages
• Not designed for extreme-resolution or specialized scientific images without domain adaptation

Bias, Risks, and Limitations

• The model may inherit social, cultural, and visual biases from the pre-training data of InternVL3 and public multimodal datasets.
• It may produce visual hallucinations, object misidentification, or inconsistent descriptions for blurry, occluded, or highly abstract images.
• Performance on professional vertical domains (medical, remote sensing, microscopic) is limited without further fine-tuning.
• The model does not have independent fact-checking and may generate factually incorrect multimodal outputs.

Recommendations

All outputs used in professional or production environments must be reviewed by qualified personnel. For deployment involving user data or public scenarios, content safety and privacy protection mechanisms are strongly recommended. Professional visual modules should be used for high-precision tasks such as medical or industrial analysis. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModel, AutoTokenizer

model_name = "Yougen/InternVL3Fangwusha8B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True
).eval()

# Example usage:
# image = load_image("your_image_file.jpg")
# question = "请详细分析这张图片中的内容和结构"
# response = model.chat(tokenizer, image, question)
# print(response)

Training Details

Training Data

Training data consists of high-quality Chinese image-text pairs, complex document images, table data, daily and industrial scene photos, and multi-turn instruction-based multimodal dialogue. All data is processed with deduplication, noise filtering, and quality control.

Training Procedure

Preprocessing [optional]

• Image normalization, resizing, and enhancement
• Text cleaning and instruction template formatting
• Multimodal sequence tokenization and alignment
• Filtering of low-quality or noisy image-text pairs

Training Hyperparameters

• Training regime: bf16 mixed precision
• Learning rate: 1.8e-5
• Batch size: 8
• Optimizer: AdamW
• Weight decay: 0.01
• Epochs: 2

Speeds, Sizes, Times [optional]

• Model size: 8B parameters
• Training hardware: NVIDIA A100 / RTX 4090 / H100 GPUs
• Training duration: Multiple hours to one day

Evaluation

Testing Data, Factors & Metrics

Testing Data

Internal Chinese multimodal benchmark including VQA, document analysis, table extraction, and complex visual reasoning.

Factors

Image complexity, layout structure, text density, scene domain, multi-turn interaction depth.

Metrics

• VQA accuracy
• Document structure and table extraction accuracy
• BLEU, ROUGE, CIDEr for captioning
• OCR accuracy + semantic consistency
• Human evaluation of rationality and fluency

Results

[More Information Needed]

Summary

The model achieves strong performance in complex Chinese multimodal understanding and reasoning, suitable for enterprise-grade and advanced research visual-language tasks.

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: NVIDIA A100 / H100 / RTX 4090
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

Vision-language architecture with powerful visual encoder and large language decoder, based on InternVL3-8B. Optimized for Chinese cross-modal alignment, complex visual reasoning, and structured document understanding.

Compute Infrastructure

Hardware

NVIDIA GPU with CUDA and large VRAM support

Software

• PyTorch
• Hugging Face Transformers & Accelerate
• TorchVision
• Pillow
• OpenCV (optional)

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

• VLM: Vision-Language Model, capable of understanding both images and text.
• InternVL3: Open-source vision-language model series developed by the InternLM research team.
• Multimodal Alignment: The ability to map visual features to language representations correctly.

More Information [optional]

For updates, feedback, or usage questions, please refer to the model repository on the Hugging Face Hub.

Model Card Authors [optional]

Yougen Yuan

Model Card Contact

[More Information Needed]