Bimokuncoro
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medgemma-4b-crc-finetuned

@@ -1,200 +1,114 @@
 ---
-library_name: transformers
 tags:
-- unsloth
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+base_model: google/medgemma-4b-it
+library_name: peft
 tags:
+  - medical
+  - vision-language
+  - histopathology
+  - colorectal-cancer
+  - lora
+  - unsloth
+license: apache-2.0
 ---
+# MedGemma 4B — CRC Tissue Classification (Fine-tuned)
+Fine-tuned version of [MedGemma 4B](https://huggingface.co/google/medgemma-4b-it)
+for 9-class colorectal cancer tissue classification from H&E stained histological images.
+## Model Description
+This model was fine-tuned using LoRA on the
+[NCT-CRC-HE-100K](https://huggingface.co/datasets/1aurent/NCT-CRC-HE) dataset
+to classify colorectal tissue patches into 9 classes:
+| Label | Tissue Type |
+|---|---|
+| ADI | Adipose |
+| BACK | Background |
+| DEB | Debris |
+| LYM | Lymphocytes |
+| MUC | Mucus |
+| MUS | Smooth Muscle |
+| NORM | Normal Colon Mucosa |
+| STR | Cancer-Associated Stroma |
+| TUM | Colorectal Adenocarcinoma Epithelium |
 ## Training Details
+| Parameter | Value |
+|---|---|
+| Base model | unsloth/medgemma-4b-it (full 16-bit) |
+| Fine-tuning method | LoRA (r=16, alpha=16, all-linear) |
+| Trainable parameters | 38,497,792 / 4,338,577,264 (0.89%) |
+| Training samples | 9,000 from NCT-CRC-HE-100K |
+| Steps | 300 (epoch = 0.267) |
+| Batch size | 4 per device, grad accum 2 (effective = 8) |
+| Learning rate | 2e-4 with cosine scheduler |
+| Optimizer | AdamW (fused) |
+| Hardware | NVIDIA A100-SXM4-80GB |
+| Training time | ~21 minutes |
+| Final training loss | 0.2072 |
+## Evaluation Results
+Evaluated on 500 randomly sampled images (seed=42) from each dataset:
+| Model | Dataset | Accuracy | Weighted F1 |
+|---|---|---|---|
+| Pretrained MedGemma | NCT-CRC-HE-100K (test) | 19.4% | 0.163 |
+| **Fine-tuned (Ours)** | NCT-CRC-HE-100K (test) | **94.2%** | **0.943** |
+| Pretrained MedGemma | CRC-VAL-HE-7K | 17.4% | 0.147 |
+| **Fine-tuned (Ours)** | CRC-VAL-HE-7K | **94.8%** | **0.948** |
+## How to Use
+```python
+from unsloth import FastVisionModel
+from peft import PeftModel
+import torch
+# Load base model + adapter
+model, processor = FastVisionModel.from_pretrained(
+    "unsloth/medgemma-4b-it",
+    load_in_4bit=False,
+)
+model = PeftModel.from_pretrained(model, "Bimokuncoro/medgemma-4b-crc-finetuned")
+FastVisionModel.for_inference(model)
+# Run inference
+from PIL import Image
+image = Image.open("your_tissue_image.png")
+messages = [{
+    "role": "user",
+    "content": [
+        {"type": "image", "image": image},
+        {"type": "text", "text": "What type of tissue is shown in this histological image?
+        Reply with ONLY one word from this list: ADI, BACK, DEB, LYM, MUC, MUS, NORM, STR, TUM.
+        Do not explain."}
+    ]
+}]
+input_text = processor.tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=False
+)
+inputs = processor(images=image, text=input_text,
+                   add_special_tokens=False, return_tensors="pt").to("cuda")
+input_length = inputs["input_ids"].shape[1]
+with torch.no_grad():
+    out = model.generate(**inputs, max_new_tokens=50, use_cache=True)
+new_tokens = out[0][input_length:]
+result = processor.tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
+print(result)  # e.g. "TUM"
+```
+## Dataset
+- **Training:** [1aurent/NCT-CRC-HE](https://huggingface.co/datasets/1aurent/NCT-CRC-HE),
+  split `NCT_CRC_HE_100K`, 9,000 samples
+- **Evaluation A:** Same dataset, held-out test split, 500 samples
+- **Evaluation B:** Split `CRC_VAL_HE_7K`, 500 samples (never seen during training)
+## Citation / Acknowledgements
+- Base model: [Google MedGemma](https://huggingface.co/google/medgemma-4b-it)
+- Fine-tuning framework: [Unsloth](https://github.com/unslothai/unsloth)
+- Dataset: [NCT-CRC-HE by 1aurent](https://huggingface.co/datasets/1aurent/NCT-CRC-HE)