InternVL3Fangwusha14B

Model Card for InternVL3Fangwusha14B

InternVL3Fangwusha14B is a 14B-parameter vision-language model (VLM) fine-tuned from InternVL3-14B, dedicated to high-performance Chinese multimodal understanding, deep visual reasoning, complex document analysis, table structure parsing, and multi-turn interactive visual dialogue for enterprise and advanced research scenarios.

Model Details

Model Description

This model is a large-scale vision-language model built on the InternVL3-14B base architecture. It is fine-tuned to significantly improve cross-modal semantic alignment, fine-grained visual recognition, complex layout understanding, and professional scene multimodal reasoning in Chinese. It provides powerful generation and reasoning capabilities while maintaining relatively efficient inference.

• 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]: InternVL3-14B

Model Sources [optional]

• Repository: https://huggingface.co/Yougen/InternVL3Fangwusha14B
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

This model can be directly used for:

• Complex Chinese visual question answering (VQA)
• Fine-grained image understanding and detailed description generation
• Complex document analysis, table extraction, form parsing and key information mining
• Multi-turn interactive visual dialogue and logical reasoning based on images
• High-precision OCR + deep semantic understanding for scanned documents and photos

Downstream Use [optional]

Can be further fine-tuned for:

• Enterprise-level intelligent document processing and review systems
• Professional vertical-domain visual question answering (finance, law, administration)
• Multimodal RAG systems supporting image-text hybrid retrieval
• AI assistants with deep visual understanding capabilities
• Automated report generation based on charts and images

Out-of-Scope Use

• Not suitable for unregulated high-risk visual tasks (medical diagnosis, autonomous driving, industrial safety without professional certification)
• Not intended for generating harmful, illegal, pornographic, violent or privacy-violating multimodal content
• Not optimized for non-Chinese languages
• Not designed for ultra-specialized scientific images (remote sensing, microscopic, astronomical) 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, misidentification or inconsistent descriptions for blurry, highly reflective or occluded images.
• Without domain fine-tuning, performance in highly professional fields may be limited.
• The model cannot independently verify facts and may generate incorrect descriptions or reasoning.

Recommendations

All outputs in professional or production scenarios should be reviewed by qualified personnel. It is strongly recommended to configure content security and privacy protection mechanisms for public deployment. Professional dedicated models are preferred for high-precision industrial or medical visual tasks. 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/InternVL3Fangwusha14B"
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.jpg")
# question = "请详细解析这张图片中的表格数据和内容"
# response = model.chat(tokenizer, image, question)
# print(response)

Training Details

Training Data

Training data includes high-quality Chinese image-text pairs, complex documents, tables, charts, professional scene images, and multi-turn instruction-based multimodal dialogue. Data has been strictly processed with deduplication, noise filtering, and quality control.

Training Procedure

Preprocessing [optional]

• Image resizing, normalization and enhancement
• Text cleaning and standardized instruction formatting
• Multimodal sequence alignment and tokenization
• Filtering low-quality, duplicated or sensitive data

Training Hyperparameters

• Training regime: bf16 mixed precision
• Learning rate: 1.5e-5
• Batch size: 8
• Optimizer: AdamW
• Weight decay: 0.01
• Epochs: 2

Speeds, Sizes, Times [optional]

• Model size: 14B parameters
• Training hardware: NVIDIA A100 / H100 GPU cluster
• Training duration: Several days

Evaluation

Testing Data, Factors & Metrics

Testing Data

Internal Chinese multimodal evaluation set covering VQA, document analysis, table extraction, chart understanding and complex visual reasoning.

Factors

Image complexity, layout density, text definition, domain professionalism, multi-turn dialogue depth.

Metrics

• VQA accuracy
• Table & structure extraction accuracy
• OCR accuracy + semantic consistency
• BLEU, CIDEr, ROUGE for generation
• Human evaluation of rationality and fluency

Results

[More Information Needed]

Summary

The model delivers strong performance in complex Chinese multimodal understanding and reasoning, suitable for high-demand enterprise 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
• 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 high-capacity visual encoder and large language decoder, based on InternVL3-14B. Optimized for Chinese cross-modal alignment, fine-grained visual understanding, and complex document reasoning.

Compute Infrastructure

Hardware

NVIDIA high-performance GPU cluster with large VRAM

Software

• PyTorch
• Hugging Face Transformers & Accelerate
• TorchVision
• Pillow
• FlashAttention

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

• VLM: Vision-Language Model that unifies visual and language understanding.
• InternVL3: Advanced vision-language model series developed by the InternLM team.
• Multimodal Reasoning: The ability to perform logical inference based on both image and text.

More Information [optional]

For updates and issues, please visit the model repository on Hugging Face Hub.

Model Card Authors [optional]

Yougen Yuan

Model Card Contact

[More Information Needed]