Duplicated from sabaridsnfuji/FloorPlanVisionAIAdaptor

.gitattributes

@@ -33,3 +33,5 @@ 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
+5_1.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,186 @@
----
-license: mit
----
+---
+base_model: unsloth/llama-3.2-11b-vision-instruct-bnb-4bit
+library_name: peft
+---
+
+# Model Card: LlamaFloorPlanVisionAIAdaptor
+
+## Model Details
+
+**Model Name:** FloorPlanVisionAIAdaptor  
+**Task:** Floor plan analysis for architectural and interior design insights  
+**Framework:** PyTorch with `unsloth`
+
+---
+
+## Model Description
+
+The `FloorPlanVisionAIAdaptor` model is a state-of-the-art Vision-Language Model (VLM) designed for analyzing floor plan images. The model leverages a deep neural architecture optimized for tasks requiring detailed visual understanding combined with textual reasoning. It can infer the layout, room counts, key features, and other architectural details from images of floor plans.
+
+### Key Features:
+- **Multi-modal Input:** Accepts both image and text input for contextual understanding.
+- **Expertise Emulation:** Simulates the expertise of an architect or interior designer.
+- **Gradient Checkpointing:** Reduces memory usage, enabling analysis of high-resolution images.
+- **Flexible Precision:** Supports 4-bit  inference depending on memory constraints.
+
+### Applications:
+- Automated floor plan analysis for real estate listings.
+- Assisting architects in creating and verifying designs.
+- Generating insights for interior design and space planning.
+- Educational purposes in architecture and design training.
+
+---
+
+## Intended Use
+
+### Primary Use Cases:
+- To analyze and interpret floor plan images, providing detailed descriptions of:
+  - Room layout and connections.
+  - Room dimensions and count.
+  - Unique architectural features.
+- To assist architects, designers, and real estate professionals in understanding and documenting floor plans.
+
+### Users:
+- Architects
+- Interior Designers
+- Real Estate Professionals
+- Educators and Students in Architecture and Design
+
+---
+
+## How to Use the Model
+
+### Installation
+To use the model, ensure that the required libraries such as `torch`, `unsloth`, and `transformers` are installed:
+```bash
+pip install torch unsloth transformers
+```
+
+### Loading the Model
+The following Python script demonstrates how to load and use the model:
+```python
+import os
+from unsloth import FastVisionModel  # Import FastVisionModel for Vision-Language tasks
+import torch
+
+# Load the pre-trained model and tokenizer
+model, tokenizer = FastVisionModel.from_pretrained(
+    "sabaridsnfuji/FloorPlanVisionAIAdaptor",
+    load_in_4bit=True,  # Use 4-bit precision to save memory if needed
+    use_gradient_checkpointing="unsloth"  # Enable gradient checkpointing for efficiency
+)
+
+FastVisionModel.for_inference(model)  # Enable inference mode
+
+
+from PIL import Image
+
+# Function to load image using PIL and return image object
+def load_image(image_path):
+    try:
+        image = Image.open(image_path)
+        return image
+    except Exception as e:
+        print(f"Error loading image {image_path}: {e}")
+        return None
+
+# Define the instruction and input
+instruction = """You are an expert in architecture and interior design. Analyze the floor plan image and describe accurately the key features, room count, layout, and any other important details you observe."""
+
+image = load_image("/content/sample_images/5_2.jpg") # converted_dataset[0]["image"]
+
+
+# Format input message
+messages = [
+    {"role": "user", "content": [
+        {"type": "image"},
+        {"type": "text", "text": instruction}
+    ]}
+]
+
+input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+
+# Prepare inputs
+inputs = tokenizer(
+    image,  # Replace with the actual image tensor
+    input_text,
+    add_special_tokens=False,
+    return_tensors="pt",
+).to("cuda")
+
+# Perform inference
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+
+output = model.generate(
+    **inputs,
+    streamer=text_streamer,
+    max_new_tokens=2048,
+    use_cache=True,
+    temperature=1.5,
+    min_p=0.1
+)
+```
+## Input image:
+
+
+![Floor Plan Image](https://huggingface.co/sabaridsnfuji/FloorPlanVisionAIAdaptor/resolve/main/5_1.jpg)
+
+
+##  Sample Output:
+
+```
+**Room Count:**
+1 bedroom, 1 study/office, 1 bathroom, kitchen, living room, dining room, verandah.
+
+**Room Types and Labels:**
+Bedroom, kitchen, living room, dining room, study/office, bathroom, verandah.
+
+**Room Sizes:**
+- Bedroom: 9'8" x 9'10"
+- Kitchen: 22'8" x 13'0"
+- Dining Room: 10'0" x 13'0"
+- Living Room: 13'8" x 15'6"
+- Study/Office: 9'8" x 9'10"
+
+**Primary Features:**
+Stairs, verandah, windows along perimeter, kitchen island.
+
+**Functional Areas:**
+Bathroom adjacent to kitchen; no pantry or mudroom. Kitchen island provides functional space.
+
+**Layout Overview:**
+Central stairs with rooms radiating off. Kitchen near bathroom; living and dining areas open-plan.
+
+**Flooring and Attributes:**
+Tile in bathroom, verandah, and main living spaces. Likely standard ceiling height.
+
+**Summary:**
+Compact, single-floor layout with essential living spaces and utility rooms. Open-plan living areas provide fluid movement; stairs likely provide additional storage.<|eot_id|>
+```
+
+
+---
+
+## Limitations
+- **Domain-Specific Knowledge:** While the model is trained to emulate architectural expertise, it may not replace human professionals for complex design tasks.
+- **Image Quality:** Performance may degrade for low-resolution or incomplete floor plans.
+- **Generalization:** The model may struggle with floor plans featuring unconventional layouts or non-standard symbols.
+
+---
+
+## Training Data
+The model was trained on a curated dataset of architectural floor plans, annotated with detailed descriptions of rooms, layouts, and features.
+
+---
+
+
+
+---
+
+## Acknowledgements
+The development of this model was inspired by advancements in multi-modal AI and the need for intelligent systems in the architectural domain. Special thanks to the contributors of `unsloth` and `transformers` libraries.
+
+---
+
+

adapter_config.json

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+{
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+  "task_type": "CAUSAL_LM",
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+  "use_rslora": false
+}

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chat_template.json

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+}

preprocessor_config.json

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+      "special": true
+    },
+    "128239": {
+      "content": "<|reserved_special_token_230|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128240": {
+      "content": "<|reserved_special_token_231|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128241": {
+      "content": "<|reserved_special_token_232|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128242": {
+      "content": "<|reserved_special_token_233|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_235|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128256": {
+      "content": "<|image|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- if strftime_now is defined %}\n        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n    {%- else %}\n        {%- set date_string = \"26 Jul 2024\" %}\n    {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- Find out if there are any images #}\n{% set image_ns = namespace(has_images=false) %}      \n{%- for message in messages %}\n    {%- for content in message['content'] %}\n        {%- if content['type'] == 'image' %}\n            {%- set image_ns.has_images = true %}\n        {%- endif %}\n    {%- endfor %}\n{%- endfor %}\n\n{#- Error out if there are images and system message #}\n{%- if image_ns.has_images and not system_message == \"\" %}\n    {{- raise_exception(\"Prompting with images is incompatible with system messages.\") }}\n{%- endif %}\n\n{#- System message if there are no images #}\n{%- if not image_ns.has_images %}\n    {{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n    {%- if tools is not none %}\n        {{- \"Environment: ipython\\n\" }}\n    {%- endif %}\n    {{- \"Cutting Knowledge Date: December 2023\\n\" }}\n    {{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n    {%- if tools is not none and not tools_in_user_message %}\n        {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n        {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n        {{- \"Do not use variables.\\n\\n\" }}\n        {%- for t in tools %}\n            {{- t | tojson(indent=4) }}\n            {{- \"\\n\\n\" }}\n        {%- endfor %}\n    {%- endif %}\n    {{- system_message }}\n    {{- \"<|eot_id|>\" }}\n{%- endif %}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n    {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' }}\n        {%- if message['content'] is string %}\n            {{- message['content'] }}\n        {%- else %}\n            {%- for content in message['content'] %}\n                {%- if content['type'] == 'image' %}\n                    {{- '<|image|>' }}\n                {%- elif content['type'] == 'text' %}\n                    {{- content['text'] }}\n                {%- endif %}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n        {{- '\"parameters\": ' }}\n        {{- tool_call.arguments | tojson }}\n        {{- \"}\" }}\n        {{- \"<|eot_id|>\" }}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|finetune_right_pad_id|>",
+  "padding_side": "right",
+  "processor_class": "MllamaProcessor",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": null
+}