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@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 lora_adapter/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 lora_adapter_vit/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+lora_adapter_agent/tokenizer.json filter=lfs diff=lfs merge=lfs -text

lora_adapter_agent/README.md

@@ -0,0 +1,117 @@
+---
+base_model: unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit
+library_name: peft
+license: apache-2.0
+language:
+- en
+pipeline_tag: image-text-to-text
+tags:
+- base_model:adapter:unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit
+- lora
+- qlora
+- sft
+- vision
+- hotel
+- cleanliness-detection
+- agentic
+- unsloth
+---
+
+# roomaudit — agentic LoRA adapter
+
+QLoRA adapter for Qwen3-VL-4B-Instruct, fine-tuned on a two-turn agentic inspection format. Part of the roomaudit project.
+
+Round 1: model receives the full room image and selects 1-2 regions worth closer inspection, returning normalised bounding box coordinates and a reason for each. Round 2: model receives the original conversation plus cropped images of those regions, then gives a final cleanliness verdict.
+
+**Note:** this adapter scores below the single-turn adapter (F1 0.736 vs 0.774) on the current synthetic dataset. The two-turn format gives the model more surface area to pick up spurious patterns from the guidance_scale=30 FLUX images. Use `lora_adapter` from this repo for better overall performance. This adapter is included as a reference for the agentic training approach.
+
+## How to use
+
+This adapter requires a two-turn inference loop. See `backend/agent.py` in the roomaudit repository for the full implementation.
+
+```python
+from huggingface_hub import snapshot_download
+from unsloth import FastVisionModel
+from peft import PeftModel
+
+# Download adapter
+snapshot_download(
+    "RanenSim/roomaudit-adapters",
+    allow_patterns="lora_adapter_agent/*",
+    local_dir="outputs/",
+)
+
+# Load
+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_agent")
+FastVisionModel.for_inference(model)
+```
+
+**Round 1 prompt** — sent with the full room image:
+```
+Before assessing this hotel room, identify 1-2 specific regions you want to inspect more closely.
+Choose regions that would give the most useful evidence about cleanliness:
+beds, pillows, floors, bins, towels, chairs are common inspection areas.
+Respond ONLY with this schema:
+{"regions": [{"region": [x1, y1, x2, y2], "reason": "..."}, ...]}
+where coordinates are fractions of the image size (0.0 to 1.0),
+x1,y1 is top-left and x2,y2 is bottom-right. Identify exactly 1 or 2 regions.
+```
+
+**Round 2 prompt** — sent with the cropped region images appended to the Round 1 conversation:
+```
+Here are the regions you requested.
+Now give your final cleanliness assessment using only this schema:
+{"clean": true/false, "defects": [{"object": "...", "type": "...", "description": "..."}]}
+Valid objects: pillow, bed_sheet, blanket, floor, carpet, chair, desk, mirror, sofa, bath_towel, bin, window.
+Valid defect types: stain, hair, debris, litter, not_emptied, dirty.
+If no defects are visible in the inspected regions, set clean=true and defects=[].
+```
+
+## Training data
+
+Same dataset as `lora_adapter`: 218 clean images, 654 synthetic messy variants (SAM3 + FLUX.1 Fill, guidance_scale=30). Balanced 1:1 by repeating each clean image 3× with cycling preferred crop objects (`pillow → bed_sheet → floor → carpet → chair → bin → bath_towel`). Total: 1308 rows, 85/15 split.
+
+Each training sample is a full two-turn conversation. Round 1 target: a JSON region list derived from SAM3 mask bounding boxes. Round 2 target: the ground-truth cleanliness verdict.
+
+## Training config
+
+| | |
+|---|---|
+| Base model | Qwen3-VL-4B-Instruct (4-bit QLoRA via Unsloth) |
+| LoRA r / alpha | 32 / 32 |
+| Target modules | attention + MLP (vision encoder frozen) |
+| lora_dropout | 0.05 |
+| Learning rate | 5e-5 (cosine, warmup 5%) |
+| Batch size | 2 × grad_accum 4 = 8 effective |
+| Epochs | 15 (early stopped at step 1225 ≈ epoch 8.8, patience=6) |
+| Optimizer | AdamW 8-bit |
+| Precision | bfloat16 |
+| Hardware | RTX 5070 Ti, 16GB VRAM |
+
+Best checkpoint: step 1075, eval_loss 0.051568
+
+## Results
+
+Evaluated on 196 held-out samples (15% split, balanced clean/messy).
+
+| Metric | Score |
+|---|---|
+| Accuracy | 0.663 |
+| Precision | 0.622 |
+| Recall | 0.902 |
+| F1 | 0.736 |
+
+Confusion matrix: TN=38, FP=56, FN=10, TP=92. Clean room accuracy 40%.
+
+![Training metrics](metrics_agent.png)
+
+## Limitations
+
+- Scores below the single-turn adapter on every metric
+- 60% false positive rate on clean rooms — the model is heavily biased toward predicting messy
+- The two-turn format likely amplifies spurious patterns from the synthetic training data: the scout step creates an "inspecting closely → defect present" association that hurts clean room classification
+- Expected to improve with higher-quality inpainting data (guidance_scale=10)

lora_adapter_agent/adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen3VLForConditionalGeneration",
+    "parent_library": "transformers.models.qwen3_vl.modeling_qwen3_vl",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/Qwen3-VL-4B-Instruct-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": "(?:.*?(?:language|text).*?(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense).*?(?:qkv|proj|linear_fc1|linear_fc2|q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj).*?)|(?:\\bmodel\\.layers\\.[\\d]{1,}\\.(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense)\\.(?:(?:qkv|proj|linear_fc1|linear_fc2|q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj)))",
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

lora_adapter_agent/adapter_model.safetensors

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+size 264316456

lora_adapter_agent/added_tokens.json

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lora_adapter_agent/chat_template.jinja

@@ -0,0 +1,120 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' }}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- for message in messages %}
+    {%- if message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content_item in message.content %}
+                {%- if 'text' in content_item %}
+                    {{- content_item.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and message.content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

lora_adapter_agent/debug.log

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lora_adapter_agent/preprocessor_config.json

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+  "crop_size": null,
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
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+  "size": {
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+    "shortest_edge": 65536
+  },
+  "temporal_patch_size": 2
+}

lora_adapter_agent/special_tokens_map.json

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lora_adapter_agent/tokenizer.json

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lora_adapter_agent/tokenizer_config.json

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+  "extra_special_tokens": {},
+  "model_max_length": 262144,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "right",
+  "processor_class": "_Unsloth_Patched_Qwen3VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

lora_adapter_agent/video_preprocessor_config.json

@@ -0,0 +1,41 @@
+{
+  "crop_size": null,
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "do_center_crop": null,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "do_sample_frames": true,
+  "fps": 2,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "input_data_format": null,
+  "max_frames": 768,
+  "merge_size": 2,
+  "min_frames": 4,
+  "num_frames": null,
+  "pad_size": null,
+  "patch_size": 16,
+  "processor_class": "_Unsloth_Patched_Qwen3VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "return_metadata": false,
+  "size": {
+    "longest_edge": 25165824,
+    "shortest_edge": 4096
+  },
+  "temporal_patch_size": 2,
+  "video_metadata": null,
+  "video_processor_type": "Qwen3VLVideoProcessor"
+}