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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/SemiMIRTLab/ATOMIC](https://github.com/SemiMIRTLab/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. |