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@@ -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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,143 @@
+---
+base_model: Qwen/Qwen3-VL-4B-Instruct
+library_name: peft
+pipeline_tag: image-text-to-text
+tags:
+- qwen3-vl
+- vision-language
+- food-recognition
+- nutrition
+- peft
+- lora
+- beforeat
+---
+
+# beforeat-food-nutrition-vision-lora
+
+`beforeat-food-nutrition-vision-lora` is a LoRA adapter for food image understanding. This checkpoint is an R&D prototype toward broader food and nutrition recognition. The current version is trained and evaluated for Food-101 dish-name recognition, not full nutrition estimation.
+
+## Model Details
+
+- **Adapter name:** `beforeat-food-nutrition-vision-lora`
+- **Base model:** `Qwen/Qwen3-VL-4B-Instruct`
+- **Adapter type:** PEFT LoRA
+- **Task:** food image to dish-name text response
+- **Current training focus:** Food-101 dish classification via instruction-style visual question answering
+- **Developed for:** Beforeat food recognition research and prototyping
+
+## Intended Use
+
+This adapter is intended for research and prototype food recognition workflows where an image of a prepared dish is provided and the model returns a concise dish-name prediction.
+
+Example prompt:
+
+```text
+Identify the dish in this image. Reply with only the exact Food-101 class name.
+```
+
+For a production app, the recommended v1 deployment path is backend inference: the mobile app uploads a food image to a server, the server runs the base Qwen3-VL model with this LoRA adapter, and the app receives the predicted dish name.
+
+## Out-of-Scope Use
+
+This checkpoint should not be treated as:
+
+- A complete nutrition estimator
+- A medical, dietary, or allergy safety tool
+- A reliable portion-size estimator
+- A production-grade model for all cuisines, restaurant conditions, or user-generated food photos
+
+The adapter currently recognizes dish names better than it estimates ingredients, calories, macros, or micronutrients.
+
+## Training Data
+
+This adapter was trained on Food-101 examples formatted as image-and-text instruction data. Food-101 is useful for dish-name research, but this checkpoint should be treated as an R&D prototype and downstream users should review the Food-101 dataset terms before any commercial or public deployment.
+
+No Food-101 images are included in this adapter repository.
+
+## Training Procedure
+
+The adapter was trained locally with PyTorch and PEFT LoRA on Apple Silicon using MPS acceleration.
+
+LoRA configuration:
+
+- **Rank:** 8
+- **Alpha:** 16
+- **Dropout:** 0.05
+- **Target modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
+- **Trainable parameters:** about 16.5M
+
+Training progression:
+
+- 1000-step Food-101 adapter
+- 1500-step broad Food-101 continuation
+- 2000-step broad Food-101 continuation
+- 2500-step mixed continuation with broad Food-101 coverage plus extra hard-class examples
+
+## Evaluation
+
+Evaluation was run on a fixed 500-image Food-101 sample.
+
+| Adapter | Strict Accuracy | Alias/Near Accuracy |
+|---|---:|---:|
+| 1000-step Food-101 LoRA | 64.60% | 78.40% |
+| 1500-step broad continuation | 79.00% | 84.80% |
+| 2000-step broad continuation | 85.20% | 87.80% |
+| 2500-step hard-only continuation | 81.80% | 82.80% |
+| **2500-step mixed continuation, this adapter** | **86.80%** | **87.60%** |
+
+The 2500-step mixed adapter is the current best strict-label checkpoint. The 2000-step broad adapter remains a close fallback because it has slightly higher alias/near scoring.
+
+Common remaining errors include visually similar food categories such as:
+
+- `tuna tartare` vs `beef tartare`
+- `chocolate cake` vs `chocolate mousse`
+- `donuts` vs `beignets`
+- `steak`, `filet mignon`, and `prime rib`
+- `bread pudding`, `panna cotta`, and dessert-adjacent classes
+
+## Loading Example
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
+
+base_model_id = "Qwen/Qwen3-VL-4B-Instruct"
+adapter_id = "YOUR_USERNAME/beforeat-food-nutrition-vision-lora"
+
+processor = AutoProcessor.from_pretrained(
+    base_model_id,
+    trust_remote_code=True,
+)
+
+model = Qwen3VLForConditionalGeneration.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+model = PeftModel.from_pretrained(model, adapter_id)
+model.eval()
+```
+
+## Limitations
+
+- The model was evaluated on Food-101-style images, not yet on a broad real-world Beforeat app photo set.
+- It may produce close but non-canonical dish names.
+- It may confuse visually similar dishes.
+- It should not be used as the sole source for dietary, medical, or nutrition decisions.
+- Broader ingredient and nutrition recognition requires additional datasets and evaluation.
+
+## Recommended Next Steps
+
+- Test this adapter against real Beforeat-style food photos.
+- Compare against the 2000-step broad adapter on real app images.
+- Add ingredient and nutrition-specific training/evaluation data before claiming nutrition estimation capability.
+- Consider a smaller distilled/mobile model for eventual on-device inference.
+
+## Framework Versions
+
+- PEFT 0.19.1
+- Transformers 4.57.0
+- PyTorch nightly used locally for Apple MPS compatibility

adapter_config.json

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+{
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+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3-VL-4B-Instruct",
+  "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": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "q_proj",
+    "gate_proj",
+    "k_proj",
+    "v_proj",
+    "o_proj",
+    "down_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+size 66134328

added_tokens.json

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chat_template.jinja

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+{%- 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 %}

preprocessor_config.json

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+  "crop_size": null,
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
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+  "do_convert_rgb": true,
+  "do_normalize": true,
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+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
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+    0.5,
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+  "image_processor_type": "Qwen2VLImageProcessorFast",
+  "image_std": [
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+  "max_pixels": 262144,
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+  "min_pixels": null,
+  "pad_size": null,
+  "patch_size": 16,
+  "processor_class": "Qwen3VLProcessor",
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+  "size": {
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+  "temporal_patch_size": 2
+}

special_tokens_map.json

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+size 11422650

tokenizer_config.json

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+}

video_preprocessor_config.json

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+{
+  "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": [
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+    0.5,
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+  "image_std": [
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+  "min_frames": 4,
+  "num_frames": null,
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+  "patch_size": 16,
+  "processor_class": "Qwen3VLProcessor",
+  "resample": 3,
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+  "return_metadata": false,
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+    "shortest_edge": 4096
+  },
+  "temporal_patch_size": 2,
+  "video_metadata": null,
+  "video_processor_type": "Qwen3VLVideoProcessor"
+}