CodingBad02
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chhaya-medgemma-lora-v2

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 ---
+license: gemma
+base_model: google/medgemma-1.5-4b-it
+library_name: peft
+pipeline_tag: image-text-to-text
+tags:
+- medgemma
+- dermatology
+- qlora
+- build-small-hackathon
 ---
+# Chhaya-MedGemma (LoRA)
+A QLoRA fine-tune of [`google/medgemma-1.5-4b-it`](https://huggingface.co/google/medgemma-1.5-4b-it)
+for **Chhaya**, a skin & heat-health companion for outdoor workers. It reads a skin
+photo and emits a structured findings JSON **directly** — no chain-of-thought
+preamble — with a `concern` level calibrated against real clinical labels.
+## Usage
+```python
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+from peft import PeftModel
+base = "google/medgemma-1.5-4b-it"
+proc = AutoProcessor.from_pretrained(base)
+model = AutoModelForImageTextToText.from_pretrained(base, torch_dtype=torch.bfloat16).to("cuda")
+model = PeftModel.from_pretrained(model, "CodingBad02/chhaya-medgemma-lora-v2").merge_and_unload()
+messages = [{"role": "user", "content": [
+    {"type": "image", "image": img},          # image BEFORE text
+    {"type": "text", "text": "skin check"},
+]}]
+inputs = proc.apply_chat_template(messages, add_generation_prompt=True,
+    tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
+out = model.generate(**inputs, max_new_tokens=400, do_sample=False)
+print(proc.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+Output schema:
+```json
+{"what_i_see","spot":{"type","color","borders","symmetry","texture"},
+ "heat_sun_signals":[],"concern":"low|watch|see_doctor","concern_reason",
+ "image_quality":"good|limited","summary"}
+```
+## Training
+- Data: [`CodingBad02/chhaya-skin-extract`](https://huggingface.co/datasets/CodingBad02/chhaya-skin-extract)
+  (ISIC-2024 biopsy-anchored + SCIN real-photo, 1,406 rows). `see_doctor` class
+  oversampled 3× to improve recall.
+- QLoRA (4-bit nf4, r=16, α=32), **frozen vision tower** (only the language model
+  adapts), 2 epochs, A100. ~$6 of compute.
+## Eval (141-image held-out test set, vs base)
+| metric | base | this model (v2) |
+|---|---|---|
+| Valid JSON | 0.993 | **1.0** |
+| Concern accuracy | 0.333 | **0.695** |
+| Malignant recall | 0.936 | 0.83 |
+| Output tokens/answer | 770 | **156** |
+Base's higher recall is achieved by labelling 60% of cases "watch" (33% accuracy);
+this model gives a real triage at 5× fewer tokens. Residual under-warning risk is
+mitigated by a deterministic ABCDE backstop in the app.
+## Limitations
+**Not a medical device. Does not diagnose.** A research/education demo built for a
+hackathon. Misses some malignant-type lesions (recall 0.83). Always pair with
+clinician review. No heat-rash/sunburn class in training (ISIC/SCIN gap).
+Inspired by [Sunny](https://github.com/mrdbourke/sunny) by Daniel Bourke.