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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- vision
+- hotel
+- cleanliness-detection
+- lora
+- qlora
+- unsloth
+- qwen3-vl
+---
+
+# RoomAudit LoRA Adapters
+
+QLoRA adapters for hotel room cleanliness detection, fine-tuned on Qwen3-VL-4B-Instruct. Part of the roomaudit project.
+
+Three adapters are included here, each from a different training approach. All were trained on the same synthetic dataset: 218 clean hotel room images with defects painted in using SAM3 + FLUX.1 Fill inpainting.
+
+---
+
+## Adapters
+
+### `lora_adapter` — primary adapter, use this one
+
+Single-turn format. Takes a room image, returns a JSON verdict with clean/messy classification and a defect list.
+
+| Metric | Score |
+|---|---|
+| Accuracy | 0.714 |
+| Precision | 0.676 |
+| Recall | 0.906 |
+| F1 | 0.774 |
+
+### `lora_adapter_agent` — agentic (two-turn) adapter
+
+Two-turn format: Round 1 selects 1-2 regions to inspect, Round 2 gives the final verdict after seeing the crops. Scores below the single-turn adapter on the current synthetic dataset. Included as a reference for the agentic training approach.
+
+| Metric | Score |
+|---|---|
+| Accuracy | 0.663 |
+| Precision | 0.622 |
+| Recall | 0.902 |
+| F1 | 0.736 |
+
+### `lora_adapter_vit` — ViT + LLM adapter
+
+Same single-turn format as the primary adapter, but with LoRA applied to the vision encoder as well as the language layers. Worse than LLM-only training: the ViT adapters learn to detect FLUX inpainting artefacts rather than actual room defects. Included as a reference.
+
+| Metric | Score |
+|---|---|
+| Accuracy | 0.587 |
+| Precision | 0.568 |
+| Recall | 0.991 |
+| F1 | 0.722 |
+
+---
+
+## Quickstart
+
+```python
+from huggingface_hub import snapshot_download
+from unsloth import FastVisionModel
+from peft import PeftModel
+from PIL import Image
+import json, re
+from qwen_vl_utils import process_vision_info
+
+snapshot_download(
+    "RanenSim/RoomAudit-Lora",
+    allow_patterns="lora_adapter/*",
+    local_dir="outputs/",
+)
+
+model, tokenizer = FastVisionModel.from_pretrained(
+    "unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit",
+    load_in_4bit=True,
+)
+model = PeftModel.from_pretrained(model, "outputs/lora_adapter")
+FastVisionModel.for_inference(model)
+
+image = Image.open("room.jpg").convert("RGB")
+messages = [
+    {"role": "system", "content": [{"type": "text", "text": "You are a hotel room cleanliness inspector. Respond ONLY with valid JSON."}]},
+    {"role": "user", "content": [
+        {"type": "image", "image": image},
+        {"type": "text", "text": '{"clean": true/false, "defects": [{"object": "...", "type": "...", "description": "..."}]}'},
+    ]},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+image_inputs, _ = process_vision_info(messages)
+inputs = tokenizer(text=[text], images=image_inputs, padding=True, return_tensors="pt").to("cuda")
+out_ids = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.1)
+output = tokenizer.decode(out_ids[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+result = json.loads(re.search(r"\{.*\}", output, re.DOTALL).group())
+```
+
+See each adapter's README for full usage instructions, training config, and results.
+
+---
+
+Source code, training notebooks, and data generation pipeline: [github.com/Razorbird360/roomaudit](https://github.com/Razorbird360/roomaudit)