Uploads model weight

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,113 +1,58 @@
 ---
-license: gemma
 base_model: google/gemma-3-4b-it
+library_name: transformers
+model_name: checkpoints
 tags:
-- vision-language-model
-- TEM
-- microscopy
-- materials-science
-- gemma
-- scientific-VLM
-language:
-- en
-pipeline_tag: image-text-to-text
+- generated_from_trainer
+- trl
+- sft
+licence: license
 ---
 
-# ATOMIC-Gemma
+# Model Card for checkpoints
 
-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.
+This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
+It has been trained using [TRL](https://github.com/huggingface/trl).
 
-> **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.
+## Quick start
 
-For the published paper and full pipeline, please refer to our GitHub repository:
-👉 [https://github.com/SemiMIRTLab/ATOMIC](https://github.com/SemiMIRTLab/ATOMIC)
+```python
+from transformers import pipeline
 
----
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
 
-## Model Details
+## Training procedure
 
-| | |
-|---|---|
-| **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)
-```
+This model was trained with SFT.
 
----
+### Framework versions
 
-## Training Data
+- TRL: 1.4.0
+- Transformers: 5.9.0
+- Pytorch: 2.12.0.dev20260407+cu128
+- Datasets: 4.8.5
+- Tokenizers: 0.22.2
 
-Training data is available on HuggingFace:
-👉 [https://huggingface.co/datasets/LabSmart/ATOMIC_dataset](https://huggingface.co/datasets/LabSmart/ATOMIC_dataset)
+## Citations
 
----
 
-## Citation
 
+Cite TRL as:
+    
 ```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}
+@software{vonwerra2020trl,
+  title   = {{TRL: Transformers Reinforcement Learning}},
+  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
+  license = {Apache-2.0},
+  url     = {https://github.com/huggingface/trl},
+  year    = {2020}
 }
-```
-
----
-
-## 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.
+```

chat_template.jinja

@@ -0,0 +1,47 @@
+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{'<start_of_turn>model
+'}}
+{%- endif -%}

config.json

@@ -0,0 +1,108 @@
+{
+  "architectures": [
+    "Gemma3ForConditionalGeneration"
+  ],
+  "boi_token_index": 255999,
+  "bos_token_id": 2,
+  "dtype": "bfloat16",
+  "eoi_token_index": 256000,
+  "eos_token_id": 1,
+  "image_token_index": 262144,
+  "initializer_range": 0.02,
+  "mm_tokens_per_image": 256,
+  "model_type": "gemma3",
+  "pad_token_id": 0,
+  "text_config": {
+    "_sliding_window_pattern": 6,
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_logit_softcapping": null,
+    "bos_token_id": 2,
+    "dtype": "bfloat16",
+    "eos_token_id": 1,
+    "final_logit_softcapping": null,
+    "head_dim": 256,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 10240,
+    "layer_types": [
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention"
+    ],
+    "max_position_embeddings": 131072,
+    "model_type": "gemma3_text",
+    "num_attention_heads": 8,
+    "num_hidden_layers": 34,
+    "num_key_value_heads": 4,
+    "pad_token_id": 0,
+    "query_pre_attn_scalar": 256,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
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+        "factor": 8.0,
+        "rope_theta": 1000000.0,
+        "rope_type": "linear"
+      },
+      "sliding_attention": {
+        "rope_theta": 10000.0,
+        "rope_type": "default"
+      }
+    },
+    "sliding_window": 1024,
+    "tie_word_embeddings": true,
+    "use_bidirectional_attention": false,
+    "use_cache": true,
+    "vocab_size": 262208
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.9.0",
+  "use_cache": false,
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "image_size": 896,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 27,
+    "patch_size": 14,
+    "vision_use_head": false
+  }
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    1,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "5.9.0"
+}

model.safetensors

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+oid sha256:801ead1729fe11287a922b5c8b442cdb6030280a644513d8e5eef0b34fb6ae29
+size 8600278008

processor_config.json

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+{
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+    "do_convert_rgb": null,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Gemma3ImageProcessor",
+    "image_seq_length": 256,
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "resample": 2,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
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+      "width": 896
+    }
+  },
+  "image_seq_length": 256,
+  "processor_class": "Gemma3Processor"
+}

tokenizer.json

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tokenizer_config.json

@@ -0,0 +1,25 @@
+{
+  "backend": "tokenizers",
+  "boi_token": "<start_of_image>",
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eoi_token": "<end_of_image>",
+  "eos_token": "<eos>",
+  "image_token": "<image_soft_token>",
+  "is_local": false,
+  "local_files_only": false,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "model_specific_special_tokens": {
+    "boi_token": "<start_of_image>",
+    "eoi_token": "<end_of_image>",
+    "image_token": "<image_soft_token>"
+  },
+  "pad_token": "<pad>",
+  "processor_class": "Gemma3Processor",
+  "sp_model_kwargs": null,
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

training_args.bin

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+size 5777