Update README.md

README.md

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 ---
 license: gemma
+base_model: google/gemma-3-4b-it
+tags:
+- vision-language-model
+- TEM
+- microscopy
+- materials-science
+- gemma
+- scientific-VLM
+language:
+- en
+pipeline_tag: image-text-to-text
 ---
+
+# ATOMIC-Gemma
+
+ATOMIC-Gemma is a domain-specific Vision-Language Model for Transmission Electron Microscopy (TEM), fine-tuned from Gemma3-4B-IT using Stage 2 instruction tuning on TEM conversation data.
+
+> **Note:** ATOMIC-Gemma is developed after the ECCV 2026 submission deadline and is **not part of the published paper**. It is released here to demonstrate the generalizability of the ATOMIC training pipeline across different base model architectures.
+
+For the published paper and full pipeline, please refer to our GitHub repository:
+👉 [https://github.com/SemiMRTLab-NCKU/ATOMIC](https://github.com/SemiMRTLab-NCKU/ATOMIC)
+
+---
+
+## Model Details
+
+| | |
+|---|---|
+| **Base Model** | Gemma3-4B-IT (`google/gemma-3-4b-it`) |
+| **Training Stage** | Stage 2 (instruction tuning) only |
+| **Training Data** | 60K Stage 2 conversations |
+| **Domain** | Transmission Electron Microscopy (TEM) |
+| **Modalities** | CTEM, HR-TEM, STEM, Diffraction |
+
+---
+
+## Inference
+
+ATOMIC-Gemma can be loaded directly via `transformers`:
+
+```python
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration
+from PIL import Image
+import torch
+
+model_id = "LabSmart/ATOMIC-Gemma"
+
+model = Gemma3ForConditionalGeneration.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+).eval()
+processor = AutoProcessor.from_pretrained(model_id)
+
+image = Image.open("your_TEM_image.png").convert("RGB")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": "What type of TEM image is this?"}
+        ]
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt"
+).to(model.device, dtype=torch.bfloat16)
+
+input_len = inputs["input_ids"].shape[-1]
+
+with torch.inference_mode():
+    generation = model.generate(**inputs, max_new_tokens=256, do_sample=False)
+
+generation = generation[0][input_len:]
+response = processor.decode(generation, skip_special_tokens=True)
+print(response)
+```
+
+---
+
+## 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 [Gemma Terms of Use](https://ai.google.dev/gemma/terms). It is intended for academic research purposes only.