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README.md

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 license: apache-2.0
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+language:
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+---
+
+# 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.
+
+```python
+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](sslocal://flow/file_open?url=https%3A%2F%2Fmlco2.github.io%2Fimpact%23compute&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=) presented in [Lacoste et al. (2019)](sslocal://flow/file_open?url=https%3A%2F%2Farxiv.org%2Fabs%2F1910.09700&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=).
+
+- **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]