Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-VL-8B-Instruct
+tags:
+- vision-language-model
+- image-text-to-text
+- tibetan
+- qwen3_vl
+- finetuned
+language:
+- bo
+- zh
+pipeline_tag: image-text-to-text
+---
+
+# FTib-VLM
+
+FTib-VLM is a Tibetan vision-language model fine-tuned from `Qwen/Qwen3-VL-8B-Instruct` for multimodal understanding in low-resource language settings. It is released as part of the FTibSuite project to support reproducible Tibetan multimodal research.
+
+## Model Details
+
+- **Model**: `onedday/FTib-VLM`
+- **Base model**: `Qwen/Qwen3-VL-8B-Instruct`
+- **Architecture**: `qwen3_vl`
+- **Parameters**: ~8.8B
+- **License**: `Apache-2.0`
+
+## Highlights
+
+- Fine-tuned for **Tibetan multimodal understanding**
+- Built on top of a strong open-source VLM backbone
+- Trained with a **three-stage adaptation pipeline**
+- Released for **research, evaluation, and downstream adaptation**
+- Shows strong improvements on Tibetan multimodal benchmarks
+
+## Intended Use
+
+FTib-VLM is intended for research and experimental applications such as:
+
+- Tibetan image question answering
+- Tibetan image description
+- Multimodal instruction following
+- Tibetan-oriented visual reasoning
+- Low-resource vision-language adaptation research
+
+## Training Overview
+
+FTib-VLM is fine-tuned from `Qwen/Qwen3-VL-8B-Instruct` using a three-stage pipeline:
+
+1. **Continual Pretraining** on Tibetan-oriented text data  
+2. **Multimodal Alignment** on Tibetan image-text pairs  
+3. **Multimodal Instruction Tuning** on Tibetan multimodal instruction data  
+
+The goal is to improve Tibetan multimodal capability while preserving the strengths of the base vision-language model.
+
+## Benchmark Summary
+
+FTib-VLM shows clear improvements over the base model on Tibetan multimodal evaluation, including:
+
+- BinaryVQA: 76.01
+- MMBench: 67.78
+- POPE-random: 80.56
+
+## Usage
+
+Install dependencies:
+
+```bash
+pip install -U transformers accelerate torch pillow
+``` 
+
+## Example
+Replace `"example.jpg"` with your local image path.
+
+```python
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForVision2Seq
+import torch
+
+model_id = "onedday/FTib-VLM"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForVision2Seq.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+image = Image.open("example.jpg").convert("RGB")
+prompt = "请详细描述这张图片。"
+
+inputs = processor(
+    text=prompt,
+    images=image,
+    return_tensors="pt",
+)
+
+inputs = {
+    k: v.to(model.device) if hasattr(v, "to") else v
+    for k, v in inputs.items()
+}
+
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=256,
+)
+
+output = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+
+print(output[0])
+``` 
+## Limitations
+
+- OCR and in-image text understanding remain challenging.
+- Benchmark performance does not fully reflect real-world reliability.
+- The model is intended primarily for research use.
+- As a low-resource adapted model, output quality may vary across domains and prompt styles.
+
+## Ethical Considerations
+
+This model is released to support Tibetan multimodal research and improve access to low-resource language technology. However, like other vision-language models, it may produce incorrect, biased, or misleading outputs. It should be used with care in high-stakes or reliability-sensitive scenarios.
+
+## Citation
+
+If you use this model, please cite the FTibSuite paper:
+
+```bibtex
+@article{xu2026ftibsuite,
+  title={FTibSuite: A Comprehensive Resource Suite for Tibetan Vision--Language Modeling},
+  author={Xu, Guixian and Liang, Yide and Su, Zeli and Song, Xuexian and Zhang, Ziyin and Dong, Yushuang and Zhang, Ting and Han, Xu},
+  year={2026}
+}
+You may also cite this repository as:
+@misc{onedday_ftib_vlm,
+  title        = {FTib-VLM},
+  author       = {onedday},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/onedday/FTib-VLM}}
+}
+
+```
+You may also cite this repository as:
+````bibtex
+
+@misc{onedday_ftib_vlm,
+  title        = {FTib-VLM},
+  author       = {onedday},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/onedday/FTib-VLM}}
+}
+```