Update README.md

README.md

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 ---
 license: llama2
+base_model:
+- lmsys/vicuna-7b-v1.5
+tags:
+- vision-language-model
+- TEM,microscopy
+- materials-science
+- llava
+- scientific-VLM
+language:
+- en
+pipeline_tag: image-text-to-text
 ---
+
+
+# ATOMIC-LLaVA
+
+ATOMIC-LLaVA is a domain-specific Vision-Language Model for Transmission Electron Microscopy (TEM), fine-tuned from LLaVA-v1.5-7B (Vicuna-v1.5-7B) using a two-stage training pipeline on 32,564 TEM subfigures collected from Nature portfolio journals.
+
+This model is introduced in the ECCV 2026 paper:
+
+> **ATOMIC: A Domain-Specific Vision-Language Model for Transmission Electron Microscopy**
+
+For code, evaluation scripts, and dataset, please refer to our GitHub repository:
+👉 [https://github.com/SemiMRTLab-NCKU/ATOMIC](https://github.com/SemiMRTLab-NCKU/ATOMIC)
+
+---
+
+## Model Details
+
+| | |
+|---|---|
+| **Base Model** | LLaVA-v1.5-7B (Vicuna-v1.5-7B) |
+| **Training Stage** | Stage 1 (alignment) + Stage 2 (instruction tuning) |
+| **Training Data** | 120K Stage 1 pairs + 60K Stage 2 conversations |
+| **Domain** | Transmission Electron Microscopy (TEM) |
+| **Modalities** | CTEM, HR-TEM, STEM, Diffraction |
+
+---
+
+## Important: Inference Requirements
+
+ATOMIC-LLaVA is built on LLaVA and **cannot be loaded directly via `transformers`**. Inference requires the LLaVA repository.
+
+**Step 1 — Clone LLaVA:**
+```bash
+git clone https://github.com/haotian-liu/LLaVA.git
+cd LLaVA
+pip install -e .
+```
+
+**Step 2 — Download weights:**
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="LabSmart/ATOMIC-LLaVA", local_dir="./ATOMIC-LLaVA")
+```
+
+**Step 3 — Run inference using our evaluation scripts:**
+
+Please refer to `evaluation/` in our GitHub repository for inference and evaluation scripts.
+
+---
+
+## Training Data
+
+Training data is available on HuggingFace:
+👉 [https://huggingface.co/datasets/LabSmart/ATOMIC_dataset](https://huggingface.co/datasets/LabSmart/ATOMIC_dataset)
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{atomic2026eccv,
+  title     = {ATOMIC: A Domain-Specific Vision-Language Model
+               for Transmission Electron Microscopy},
+  author    = {Tu, C. and Hsu, Shu-han and others},
+  booktitle = {Proceedings of ECCV 2026},
+  year      = {2026},
+  note      = {BibTeX will be updated upon publication}
+}
+```
+
+---
+
+## License
+
+This model is released under the [LLaMA 2 Community License](https://ai.meta.com/llama/license/). It is intended for academic research purposes only and may not be used for commercial purposes.