Upload folder using huggingface_hub

.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
+lora_adapter/tokenizer.json filter=lfs diff=lfs merge=lfs -text

lora_adapter/README.md

@@ -0,0 +1,113 @@
+---
+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
+- unsloth
+---
+
+# roomaudit — single-turn LoRA adapter
+
+QLoRA adapter for Qwen3-VL-4B-Instruct, fine-tuned to classify hotel room cleanliness and identify defects. This is the primary adapter from the roomaudit project — single-turn format, best overall results.
+
+Takes a hotel room image, returns a JSON verdict with a clean/messy classification and a list of detected defects (object, type, description).
+
+## How to use
+
+```python
+from huggingface_hub import snapshot_download
+from unsloth import FastVisionModel
+from peft import PeftModel
+from PIL import Image
+import json, re, torch
+from qwen_vl_utils import process_vision_info
+
+# Download adapter
+snapshot_download(
+    "RanenSim/roomaudit-adapters",
+    allow_patterns="lora_adapter/*",
+    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")
+FastVisionModel.for_inference(model)
+
+# Inference
+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": 'Inspect this hotel room for cleanliness defects. Respond ONLY with valid JSON using this schema: {"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())
+# {"clean": false, "defects": [{"object": "pillow", "type": "stain", "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`
+
+## Training data
+
+218 clean hotel room images (manually sourced from Unsplash, Pexels, Flickr). Messy images generated synthetically: SAM3 segments objects in each clean image, FLUX.1 Fill inpaints defects onto the segmented regions. 3 messy variants per clean image, up to 3 stacked defects each, producing 654 messy images. Dataset balanced to 1:1 by repeating each clean image 3× (no file copies, just extra rows). Total: 1308 rows, 85/15 train/eval split.
+
+Inpainting used FLUX.1 Fill Dev at guidance_scale=30. Images are realistic but some defects render more dramatically than real-world examples — this is the main data quality ceiling.
+
+## 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 | 1e-4 (cosine, warmup 5%) |
+| Batch size | 2 × grad_accum 4 = 8 effective |
+| Epochs | 4 (early stopping patience=4) |
+| Optimizer | AdamW 8-bit |
+| Precision | bfloat16 |
+| Hardware | RTX 5070 Ti, 16GB VRAM |
+
+Best checkpoint: step 350, eval_loss 0.036393
+
+## Results
+
+Evaluated on 196 held-out samples (15% split, balanced clean/messy).
+
+| Metric | Score |
+|---|---|
+| Accuracy | 0.714 |
+| Precision | 0.676 |
+| Recall | 0.906 |
+| F1 | 0.774 |
+
+![Training metrics](metrics.png)
+
+## Limitations
+
+- Trained on synthetic defects only — real messy room images may look different enough to affect accuracy
+- FLUX guidance_scale=30 produces some unrealistic defects (oversaturated stains, dramatic marks), which limits how well the model generalises to subtle real-world dirt
+- Precision at 0.676 means roughly a 32% false positive rate on clean rooms — the model leans toward predicting messy when uncertain
+- Not tested on non-hotel room images; object categories are hotel-specific

lora_adapter/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/adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b288d75b7f71ffd54ccc9bf0912d43f4a25f76bb684059bd7b6f654cdcc7297a
+size 264316456

lora_adapter/added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

lora_adapter/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/preprocessor_config.json

@@ -0,0 +1,39 @@
+{
+  "crop_size": null,
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "disable_grouping": null,
+  "do_center_crop": null,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_pad": null,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "Qwen2VLImageProcessorFast",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "input_data_format": null,
+  "max_pixels": null,
+  "merge_size": 2,
+  "min_pixels": null,
+  "pad_size": null,
+  "patch_size": 16,
+  "processor_class": "_Unsloth_Patched_Qwen3VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "return_tensors": null,
+  "size": {
+    "longest_edge": 16777216,
+    "shortest_edge": 65536
+  },
+  "temporal_patch_size": 2
+}

lora_adapter/special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

lora_adapter/tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:af560c5e5807192776a9f72a0ea33c9331816d9c59af61e02bdf766f8a2a97fb
+size 11422922

lora_adapter/tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
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+      "single_word": false,
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+    "151661": {
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+      "rstrip": false,
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+    "151663": {
+      "content": "<|repo_name|>",
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+    },
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+    },
+    "151665": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
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+      "normalized": false,
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+    "151667": {
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+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "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/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"
+}