[martin-dev] add demo v1 test

LICENSE

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README.md

@@ -1,16 +1,245 @@
----
-title: VLM Lens
-emoji: 💬
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 5.42.0
-app_file: app.py
-pinned: false
-hf_oauth: true
-hf_oauth_scopes:
-- inference-api
-license: apache-2.0
----
-
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# <img src="imgs/logo.png" alt="VLM-Lens Logo" height="48" style="vertical-align:middle; margin-right:50px;"/> VLM-Lens
+
+[![python](https://img.shields.io/badge/Python-3.10%2B-blue.svg?logo=python&style=flat-square)](https://www.python.org/downloads/release/python-31012/)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg?style=flat-square)](https://www.apache.org/licenses/LICENSE-2.0)
+[![Documentation](https://img.shields.io/badge/Documentation-Online-green.svg?style=flat-square)](https://compling-wat.github.io/vlm-lens/)
+[![Jupyter Notebook](https://img.shields.io/badge/Jupyter-Notebook-orange.svg?logo=jupyter&style=flat-square)](tutorial-notebooks/guide.ipynb)
+[![Google Colab](https://img.shields.io/badge/Google-Colab-orange?logo=googlecolab&style=flat-square)](https://colab.research.google.com/drive/13WC4HA6syXFotmn7S8WsVz4OmoHsfHV9?usp=sharing)
+
+<p align="center">
+  <img src="imgs/teaser.png" alt="VLM-Lens Teaser" width="100%" />
+</p>
+
+## Table of Contents
+
+- [Environment Setup](#environment-setup)
+- [Example Usage: Extract Qwen2-VL-2B Embeddings with VLM-Lens](#example-usage-extract-qwen2-vl-2b-embeddings-with-vlm-lens)
+  - [General Command-Line Demo](#general-command-line-demo)
+  - [Run Qwen2-VL-2B Embeddings Extraction](#run-qwen2-vl-2b-embeddings-extraction)
+- [Layers of Interest in a VLM](#layers-of-interest-in-a-vlm)
+  - [Retrieving All Named Modules](#retrieving-all-named-modules)
+  - [Matching Layers](#matching-layers)
+- [Feature Extraction using HuggingFace Datasets](#feature-extraction-using-huggingface-datasets)
+- [Output Database](#output-database)
+- [Demo: Principal Component Analysis over Primitive Concept](#principal-component-analysis-over-primitive-concept)
+- [Contributing to VLM-Lens](#contributing-to-vlm-lens)
+- [Miscellaneous](#miscellaneous)
+
+## Environment Setup
+We recommend using a virtual environment to manage your dependencies. You can create one using the following command to create a virtual environment under
+```bash
+virtualenv --no-download "venv/vlm-lens-base" --prompt "vlm-lens-base"  # Or "python3.10 -m venv venv/vlm-lens-base"
+source venv/vlm-lens-base/bin/activate
+```
+
+Then, install the required dependencies:
+```bash
+pip install --upgrade pip
+pip install -r envs/base/requirements.txt
+```
+
+There are some models that require different dependencies, and we recommend creating a separate virtual environment for each of them to avoid conflicts.
+For such models, we have offered a separate `requirements.txt` file under `envs/<model_name>/requirements.txt`, which can be installed in the same way as above.
+All the model-specific environments are independent of the base environment, and can be installed individually.
+
+**Notes**:
+1. There may be local constraints (e.g., issues caused by cluster regulations) that cause failure of the above commands. In such cases, you are encouraged to modify it whenever fit. We welcome issues and pull requests to help us keep the dependencies up to date.
+2. Some models, due to the resources available at the development time, may not be fully supported on modern GPUs. While our released environments are tested on L40s GPUs, we recommend following the error messages to adjust the environment setups for your specific hardware.
+
+## Example Usage: Extract Qwen2-VL-2B Embeddings with VLM-Lens
+
+### General Command-Line Demo
+
+The general command to run the quick command-line demo is:
+```bash
+python -m src.main \
+  --config <config-file-path> \
+  --debug
+```
+with an optional debug flag to see more detailed outputs.
+
+Note that the config file should be in yaml format, and that any arguments you want to send to the huggingface API should be under the `model` key.
+See `configs/models/qwen/qwen-2b.yaml` as an example.
+
+### Run Qwen2-VL-2B Embeddings Extraction
+The file `configs/models/qwen/qwen-2b.yaml` contains the configuration for running the Qwen2-VL-2B model.
+
+```yaml
+architecture: qwen  # Architecture of the model, see more options in src/models/configs.py
+model_path: Qwen/Qwen2-VL-2B-Instruct  # HuggingFace model path
+model:  # Model configuration, i.e., arguments to pass to the model
+  - torch_dtype: auto
+output_db: output/qwen.db  # Output database file to store embeddings
+input_dir: ./data/  # Directory containing images to process
+prompt: "Describe the color in this image in one word."  # Textual prompt
+pooling_method: None  # Pooling method to use for aggregating token embeddings over tokens (options: None, mean, max)
+modules:  # List of modules to extract embeddings from
+  - lm_head
+  - visual.blocks.31
+```
+
+To run the extraction on available GPU, use the following command:
+```bash
+python -m src.main --config configs/models/qwen/qwen-2b.yaml --debug
+```
+
+If there is no GPU available, you can run it on CPU with:
+```bash
+python -m src.main --config configs/models/qwen/qwen-2b.yaml --device cpu --debug
+```
+
+## Layers of Interest in a VLM
+### Retrieving All Named Modules
+Unfortunately there is no way to find which layers to potentially match to without loading the model. This can take quite a bit of system time figuring out.
+
+Instead, we offer some cached results under `logs/` for each model, which were generated through including the `-l` or `--log-named-modules` flag when running `python -m src.main`.
+
+When running this flag, it is not necessary to set modules or anything besides the architecture and HuggingFace model path.
+
+### Matching Layers
+To automatically set up which layers to find/use, one should use the Unix style strings, where you can use `*` to denote wildcards.
+
+For example, if one wanted to match with all the attention layer's query projection layer for Qwen, simply add the following lines to the .yaml file:
+```
+modules:
+  - model.layers.*.self_attn.q_proj
+```
+## Feature Extraction using HuggingFace Datasets
+To use VLM-Lens with either hosted or local datasets, there are multiple methods you can use depending on the location of the input images.
+
+First, your dataset must be standardized to a format that includes the attributes of `prompt`, `label` and `image_path`. Here is a snippet of the `compling/coco-val2017-obj-qa-categories` dataset, adjusted with the former attributes:
+
+| id | prompt | label | image_path |
+|---|---|---|---|
+| 397,133 | Is this A photo of a dining table on the bottom | yes | /path/to/397133.png
+| 37,777 | Is this A photo of a dining table on the top | no | /path/to/37777.png
+
+This can be achieved manually or using the helper script in `scripts/map_datasets.py`.
+
+### Method 1: Using hosted datasets
+If you are using datasets hosted on a platform such as HuggingFace, you will either use images that are also *hosted*, or ones that are *downloaded locally* with an identifier to map back to the hosted dataset (e.g., filename).
+
+You must use the `dataset_path` attribute in your configuration file with the appropriate `dataset_split` (if it exists, otherwise leave it out).
+
+#### 1(a): Hosted Dataset with Hosted Images
+```yaml
+dataset:
+  - dataset_path: compling/coco-val2017-obj-qa-categories
+  - dataset_split: val2017
+```
+
+#### 1(b): Hosted Dataset with Local Images
+
+> 🚨 **NOTE**: The `image_path` attribute in the dataset must contain either filenames or relative paths, such that a cell value of `train/00023.png` can be joined with `image_dataset_path` to form the full absolute path: `/path/to/local/images/train/00023.png`. If the `image_path` attribute does not require any additional path joining, you can leave out the `image_dataset_path` attribute.
+
+```yaml
+dataset:
+  - dataset_path: compling/coco-val2017-obj-qa-categories
+  - dataset_split: val2017
+  - image_dataset_path: /path/to/local/images  # downloaded using configs/dataset/download-coco.yaml
+```
+
+
+### Method 2: Using local datasets
+#### 2(a): Local Dataset containing Image Files
+```yaml
+dataset:
+  - local_dataset_path: /path/to/local/CLEVR
+  - dataset_split: train # leave out if unspecified
+```
+
+#### 2(b): Local Dataset with Separate Input Image Directory
+
+> 🚨 **NOTE**: The `image_path` attribute in the dataset must contain either filenames or relative paths, such that a cell value of `train/00023.png` can be joined with `image_dataset_path` to form the full absolute path: `/path/to/local/images/train/00023.png`. If the `image_path` attribute does not require any additional path joining, you can leave out the `image_dataset_path` attribute.
+
+```yaml
+dataset:
+  - local_dataset_path: /path/to/local/CLEVR
+  - dataset_split: train # leave out if unspecified
+  - image_dataset_path: /path/to/local/CLEVR/images
+```
+
+### Output Database
+Specified by the `-o` and `--output-db` flags, this specifies the specific output database we want. From this, in SQL we have a single table under the name `tensors` with the following columns:
+```
+name, architecture, timestamp, image_path, prompt, label, layer, tensor_dim, tensor
+```
+where each column contains:
+1. `name` represents the model path from HuggingFace.
+2. `architecture` is the supported flags above.
+3. `timestamp` is the specific time that the model was ran.
+4. `image_path` is the absolute path to the image.
+5. `prompt` stores the prompt used in that instance.
+6. `label` is an optional cell that stores the "ground-truth" answer, which is helpful in use cases such as classification.
+7. `layer` is the matched layer from `model.named_modules()`
+8. `pooling_method` is the pooling method used for aggregating token embeddings over tokens.
+9. `tensor_dim` is the dimension of the tensor saved.
+10. `tensor` is the embedding saved.
+
+## Principal Component Analysis over Primitive Concept
+
+### Data Collection
+
+Download license-free images for primitive concepts (e.g., colors):
+
+```bash
+pip install -r data/concepts/requirements.txt
+python -m data.concepts.download --config configs/concepts/colors.yaml
+```
+
+### Embedding Extraction
+
+Run the LLaVA model to obtain embeddings of the concept images:
+
+```bash
+python -m src.main --config configs/models/llava-7b/llava-7b-concepts-colors.yaml --device cuda
+```
+
+Also, run the LLaVA model to obtain embeddings of the test images:
+
+```bash
+python -m src.main --config configs/models/llava-7b/llava-7b.yaml --device cuda
+```
+
+### Run PCA
+
+Several PCA-based analysis scripts are provided:
+```bash
+pip install -r src/concepts/requirements.txt
+python -m src.concepts.pca
+python -m src.concepts.pca_knn
+python -m src.concepts.pca_separation
+```
+
+## Contributing to VLM-Lens
+
+We welcome contributions to VLM-Lens! If you have suggestions, improvements, or bug fixes, please consider submitting a pull request, and we are actively reviewing them.
+
+We generally follow the [Google Python Styles](https://google.github.io/styleguide/pyguide.html) to ensure readability, with a few exceptions stated in `.flake8`.
+We use pre-commit hooks to ensure code quality and consistency---please make sure to run the following scripts before committing:
+```python
+pip install pre-commit
+pre-commit install
+```
+
+
+## Miscellaneous
+
+### Using a Cache
+To use a specific cache, one should set the `HF_HOME` environment variable as so:
+```
+HF_HOME=./cache/ python -m src.main --config configs/models/clip/clip.yaml --debug
+```
+
+
+### Using Submodule-Based Models
+There are some models that require separate submodules to be cloned, such as Glamm.
+To use these models, please follow the instructions below to download the submodules.
+
+#### Glamm
+For Glamm (GroundingLMM), one needs to clone the separate submodules, which can be done with the following command:
+```
+git submodule update --recursive --init
+```
+
+See [our document](https://compling-wat.github.io/vlm-lens/tutorials/grounding-lmm.html) for details on the installation.

app.py

@@ -1,70 +1,7 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+# app.py
+from demo.launch_gradio import create_demo
 
+demo = create_demo()
 
 if __name__ == "__main__":
     demo.launch()

configs/concepts/colors.yaml

@@ -0,0 +1,69 @@
+# Creative Commons Image Downloader Configuration
+# This file defines the configuration for downloading images
+
+# Basic Settings
+num_images: 10
+normalize_size: 256
+data_dir: "./data/images/colors/"
+results_filename: "colors.json"
+
+# Search Provider Configuration
+# Options: google, bing, both
+search_provider: "both"
+retrieve_multiplier: 10
+
+# Image Processing Settings
+image_format: "JPEG"  # JPEG, PNG, WEBP
+image_quality: 90
+load_truncated_images: true
+
+# Download Settings
+max_size_mb: 10
+timeout: 15
+
+# Deduplication Settings
+enable_deduplication: true
+similarity_threshold: 5
+
+# Rate Limiting (seconds)
+request_delay_min: 0.5
+request_delay_max: 1.5
+
+# Search Queries - Color Concepts
+queries:
+  - "red"
+  - "blue"
+  - "yellow"
+  - "green"
+  - "orange"
+  - "purple"
+  - "white"
+  - "black"
+  - "gray"
+  - "silver"
+  - "gold"
+  - "pink"
+  - "brown"
+  - "beige"
+  - "crimson"
+  - "maroon"
+  - "cyan"
+  - "turquoise"
+  - "violet"
+  - "magenta"
+
+# User Agent Rotation
+user_agents:
+  - "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+  - "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+  - "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+
+# Direct Image Extensions
+direct_img_extensions:
+  - ".jpg"
+  - ".jpeg"
+  - ".png"
+  - ".gif"
+  - ".webp"
+  - ".bmp"
+  - ".tiff"

configs/dataset/download-clevr.yaml

@@ -0,0 +1,6 @@
+dataset_path: compling/CLEVR_val_categories
+split_name: [color, shape, material, size, boolean, number]
+parent_folder: CLEVR
+dataset_download_place: data/CLEVR/CLEVR_dataset
+img_url: https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip
+img_download_place: data/CLEVR/CLEVR_v1.0.zip

configs/dataset/download-coco.yaml

@@ -0,0 +1,6 @@
+dataset_path: compling/coco-val2017-obj-qa-categories
+split_name: val2017
+parent_folder: COCO
+dataset_download_place: data/COCO/COCO_dataset
+img_url: http://images.cocodataset.org/zips/val2017.zip
+img_download_place: data/COCO/val2017.zip

configs/dataset/download-gqa.yaml

@@ -0,0 +1,6 @@
+dataset_path: compling/GQA_dataset_binary
+split_name: val
+parent_folder: GQA
+dataset_download_place: data/GQA/GQA_dataset
+img_url: http://downloads.cs.stanford.edu/nlp/data/gqa/images.zip
+img_download_place: data/GQA/images.zip

configs/models/aya-vision-8b/aya-vision-8b.yaml

@@ -0,0 +1,10 @@
+architecture: aya-vision
+model_path: CohereLabs/aya-vision-8b
+model:
+  - torch_dtype: float16
+output_db: output/aya-vision.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - language_model.model.layers.15.input_layernorm
+  - language_model.model.layers.31.input_layernorm

configs/models/blip2/blip2.yaml

@@ -0,0 +1,10 @@
+architecture: blip2
+model_path: Salesforce/blip2-opt-2.7b
+model:
+  - torch_dtype: auto
+output_db: output/blip2.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - language_model.lm_head
+  - vision_model.post_layernorm

configs/models/clip/clip.yaml

@@ -0,0 +1,10 @@
+architecture: clip
+model_path: openai/clip-vit-base-patch32
+model:
+  - torch_dtype: auto
+output_db: output/clip.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - visual_projection
+  - text_projection

configs/models/cogvlm/cogvlm-chat.yaml

@@ -0,0 +1,17 @@
+architecture: cogvlm
+model_path: THUDM/cogvlm-chat-hf
+model:
+  - low_cpu_mem_usage: True
+  - trust_remote_code: True
+  - tokenizer_path: lmsys/vicuna-7b-v1.5
+  - legacy: True
+  - template_version: vqa
+forward:
+  - max_new_tokens: 2048
+  - do_sample: False
+output_db: output/cogvlm.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - model.vision.transformer.layers.62
+  - lm_head

configs/models/glamm/glamm.yaml

@@ -0,0 +1,19 @@
+architecture: glamm
+model_path: MBZUAI/GLaMM-FullScope
+model:
+  - image_size: 1024
+  - model_max_length: 1536
+  - lora_r: 8
+  - vision_tower: "openai/clip-vit-large-patch14-336"
+  - local_rank: 0
+  - use_mm_start_end: true
+  - conv_type: "llava_v1"
+forward:
+  - max_new_tokens: 1
+vis_save_path: "./vis_output"
+output_db: glamm.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - model.layers.15.post_attention_layernorm
+  - model.layers.31.post_attention_layernorm

configs/models/internlm-xc/internlm-xc.yaml

@@ -0,0 +1,13 @@
+architecture: internlm-xcomposer
+model_path: internlm/internlm-xcomposer2d5-7b
+model:
+  - torch_dtype: auto
+  - attn_implementation: eager
+forward:
+  - max_new_tokens: 1
+output_db: output/internlm.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - model.layers.31
+  - vit.vision_tower.vision_model.encoder.layers.23

configs/models/internvl/internvl.yaml

@@ -0,0 +1,14 @@
+architecture: internvl
+model_path: OpenGVLab/InternVL2_5-8B
+model:
+  - torch_dtype: bfloat16
+  - trust_remote_code: True
+forward:
+  - max_new_tokens: 1
+  - do_sample: True
+output_db: output/internvl.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - vision_model.encoder.layers.23
+  - language_model.output

configs/models/janus/janus-pro-1b.yaml

@@ -0,0 +1,14 @@
+architecture: janus
+model_path: deepseek-community/Janus-Pro-1B
+model:
+  - attn_implementation: eager
+forward:
+  - max_new_tokens: 1
+  - do_sample: True
+  - generation_mode: 'text'
+output_db: output/janus.db
+input_dir: ./data
+prompt: "Describe the color in this image in one word."
+modules:
+  - model.language_model.layers.11.post_attention_layernorm
+  - model.language_model.layers.23.post_attention_layernorm

configs/models/llava-7b/llava-7b-clevr.yaml

@@ -0,0 +1,10 @@
+architecture: llava
+model_path: llava-hf/llava-1.5-7b-hf
+dataset:
+  - dataset_path: compling/CLEVR_categories
+  - dataset_split: boolean
+output_db: output/llava-boolean.db
+pooling_method: mean
+modules:
+  - language_model.model.layers.16.post_attention_layernorm
+  - language_model.model.layers.31.post_attention_layernorm

configs/models/llava-7b/llava-7b-concepts-colors.yaml

@@ -0,0 +1,8 @@
+architecture: llava
+model_path: llava-hf/llava-1.5-7b-hf
+input_dir: ./data/images/colors/
+prompt: "Describe the color in this image in one word."
+output_db: output/llava-concepts-colors.db
+modules:
+  - vision_tower.vision_model.encoder.layers.23.layer_norm2
+  - language_model.model.layers.31.post_attention_layernorm

configs/models/llava-7b/llava-7b.yaml

@@ -0,0 +1,9 @@
+architecture: llava
+model_path: llava-hf/llava-1.5-7b-hf
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+output_db: output/llava.db
+modules:
+  - vision_tower.vision_model
+  - vision_tower.vision_model.encoder.layers.23.layer_norm2
+  - language_model.model.layers.31.post_attention_layernorm

configs/models/minicpm-V2/minicpm-V2.yaml

@@ -0,0 +1,13 @@
+architecture: minicpm
+model_path: compling/MiniCPM-V-2
+model:
+  - torch_dtype: auto
+  - trust_remote_code: True
+forward:
+  - max_new_tokens: 1
+output_db: output/minicpm.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - llm.lm_head
+  - vpm.encoder.layers.26

configs/models/minicpm-o/minicpm-o.yaml

@@ -0,0 +1,14 @@
+architecture: minicpm
+model_path: openbmb/MiniCPM-o-2_6
+model:
+  - torch_dtype: auto
+  - trust_remote_code: True
+  - attn_implementation: sdpa
+forward:
+  - max_new_tokens: 1
+output_db: output/minicpm-o.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - llm.lm_head
+  - vpm.encoder.layers.26

configs/models/molmo/molmo-7b.yaml

@@ -0,0 +1,13 @@
+architecture: molmo
+model_path: allenai/Molmo-7B-D-0924
+model:
+  - torch_dtype: auto
+forward:
+  - max_new_tokens: 1
+  - stop_strings: <|endoftext|>
+output_db: output/molmo.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - model.transformer.blocks.0
+  - model.vision_backbone.image_vit.transformer.resblocks.22

configs/models/paligemma/paligemma-3b.yaml

@@ -0,0 +1,11 @@
+architecture: paligemma
+model_path: google/paligemma-3b-mix-224
+model:
+  - torch_dtype: auto
+  - token: <HUGGINGFACE_TOKEN>
+output_db: output/paligemma.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - language_model.lm_head
+  - multi_modal_projector

configs/models/pixtral/pixtral-12b.yaml

@@ -0,0 +1,9 @@
+architecture: pixtral
+model_path: mistralai/Pixtral-12B-2409
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+output_db: output/pixtral.db
+modules:
+  - layers.19.attention_norm
+  - layers.39.attention_norm
+download_path: "/project/aip-fredashi/fredashi/huggingface/pixtral-12b" # CHANGE THIS. This is the path to store the downloaded model. This dir will be created.

configs/models/plm/plm.yaml

@@ -0,0 +1,13 @@
+architecture: plm
+model_path: facebook/Perception-LM-1B
+model:
+  - torch_dtype: auto
+  - trust_remote_code: True
+forward:
+  - max_new_tokens: 1
+output_db: output/plm.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+modules:
+  - model.language_model.layers.7.post_attention_layernorm
+  - model.language_model.layers.15.post_attention_layernorm

configs/models/qwen/qwen-2b-clevr.yaml

@@ -0,0 +1,10 @@
+architecture: qwen
+model_path: Qwen/Qwen2-VL-2B-Instruct
+dataset:
+  - dataset_path: compling/CLEVR_categories
+  - dataset_split: boolean
+output_db: output/qwen-boolean.db
+pooling_method: mean
+modules:
+  - model.layers.13.post_attention_layernorm
+  - model.layers.27.post_attention_layernorm

configs/models/qwen/qwen-2b.yaml

@@ -0,0 +1,11 @@
+architecture: qwen
+model_path: Qwen/Qwen2-VL-2B-Instruct
+model:
+  - torch_dtype: auto
+output_db: output/qwen.db
+input_dir: ./data/test-images/
+prompt: "Describe the color in this image in one word."
+pooling_method: None
+modules:
+  - lm_head
+  - visual.blocks.31

configs/probe/llava/clevr-boolean-l16.yaml

@@ -0,0 +1,18 @@
+model:
+  - activation: ReLU
+  - hidden_size: 512
+  - num_layers: 2
+  - save_dir: output/llava_boolean_probe_l16
+training:
+  - batch_size: [64, 128, 1024]
+  - num_epochs: [50, 100, 200]
+  - learning_rate: [0.001, 0.0005, 0.0001]
+  - optimizer: AdamW
+  - loss: CrossEntropyLoss
+test:
+  - batch_size: 32
+  - loss: CrossEntropyLoss
+data:
+  - input_db: output/llava-boolean.db
+  - db_name: tensors
+  - input_layer: language_model.model.layers.16.post_attention_layernorm

configs/probe/qwen/clevr-boolean-l13-example.yaml

@@ -0,0 +1,18 @@
+model:
+  - activation: ReLU
+  - hidden_size: 512
+  - num_layers: 2
+  - save_dir: output/qwen_boolean_probe_l13
+training:
+  - batch_size: [64, 128]
+  - num_epochs: [50]
+  - learning_rate: [0.001]
+  - optimizer: AdamW
+  - loss: CrossEntropyLoss
+test:
+  - batch_size: 32
+  - loss: CrossEntropyLoss
+data:
+  - input_db: output/qwen-boolean.db
+  - db_name: tensors
+  - input_layer: model.layers.13.post_attention_layernorm

configs/probe/qwen/clevr-boolean-l13.yaml

@@ -0,0 +1,18 @@
+model:
+  - activation: ReLU
+  - hidden_size: 512
+  - num_layers: 2
+  - save_dir: output/qwen_boolean_probe_l13
+training:
+  - batch_size: [64, 128, 1024]
+  - num_epochs: [50, 100, 200]
+  - learning_rate: [0.001, 0.0005, 0.0001]
+  - optimizer: AdamW
+  - loss: CrossEntropyLoss
+test:
+  - batch_size: 32
+  - loss: CrossEntropyLoss
+data:
+  - input_db: output/qwen-boolean.db
+  - db_name: tensors
+  - input_layer: model.layers.13.post_attention_layernorm

demo/.gradio/certificate.pem

@@ -0,0 +1,31 @@
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+-----END CERTIFICATE-----

demo/__init__.py

@@ -0,0 +1,6 @@
+"""Demo package."""
+
+from . import _bootstrap  # noqa: F401
+from .lookup import get_model_info  # re-export for convenience
+
+__all__ = ['get_model_info']

demo/_bootstrap.py

@@ -0,0 +1,6 @@
+import sys
+from pathlib import Path
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))

demo/launch_gradio.py

@@ -0,0 +1,645 @@
+"""Gradio demo for visualizing VLM first token probability distributions with two images."""
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.nn.functional as F
+from matplotlib.figure import Figure
+from matplotlib.text import Text
+from PIL import Image
+
+from demo.lookup import ModelVariants, get_model_info  # noqa: E402
+from src.main import get_model  # noqa: E402
+from src.models.base import ModelBase  # noqa: E402
+from src.models.config import Config, ModelSelection  # noqa: E402
+
+models_cache: Dict[str, Any] = {}
+current_model_selection: Optional[ModelSelection] = None
+
+
+def read_layer_spec(spec_file_path: str) -> List[str]:
+    """Read available layers from the model spec file.
+
+    Args:
+        spec_file_path: Path to the model specification file.
+
+    Returns:
+        List of available layer names, skipping blank lines.
+    """
+    try:
+        with open(spec_file_path, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+
+        # Filter out blank lines and strip whitespace
+        layers = [line.strip() for line in lines if line.strip()]
+        return layers
+
+    except FileNotFoundError:
+        print(f'Spec file not found: {spec_file_path}')
+        return ['Default layer (spec file not found)']
+    except Exception as e:
+        print(f'Error reading spec file: {str(e)}')
+        return ['Default layer (error reading spec)']
+
+
+def update_layer_choices(model_choice: str) -> Tuple[gr.Dropdown, gr.Button]:
+    """Update layer dropdown choices based on selected model.
+
+    Args:
+        model_choice: Selected model name.
+
+    Returns:
+        Updated dropdown component and button visibility.
+    """
+    if not model_choice:
+        return gr.Dropdown(choices=[], visible=False), gr.Button(visible=False)
+
+    try:
+        # Convert string choice to ModelVariants enum
+        model_var = ModelVariants(model_choice.lower())
+
+        # Get model info and read layer spec
+        _, _, model_spec_path = get_model_info(model_var)
+        layers = read_layer_spec(model_spec_path)
+
+        # Return updated dropdown with layer choices and make button visible
+        return (
+            gr.Dropdown(
+                choices=layers,
+                label=f'Select Module for {model_choice}',
+                value=layers[0] if layers else None,
+                visible=True,
+                interactive=True
+            ),
+            gr.Button('Analyze', variant='primary', visible=True)
+        )
+
+    except ValueError:
+        return (
+            gr.Dropdown(
+                choices=['Model not implemented'],
+                label='Select Module',
+                visible=True,
+                interactive=False
+            ),
+            gr.Button('Analyze', variant='primary', visible=False)
+        )
+    except Exception as e:
+        return (
+            gr.Dropdown(
+                choices=[f'Error: {str(e)}'],
+                label='Select Module',
+                visible=True,
+                interactive=False
+            ),
+            gr.Button('Analyze', variant='primary', visible=False)
+        )
+
+
+def load_model(model_var: ModelVariants, config: Config) -> ModelBase:
+    """Load the specified VLM and processor.
+
+    Args:
+        model_var: The model to load from ModelVariants enum.
+        config: The configuration object with model parameters.
+
+    Returns:
+        The loaded model instance.
+
+    Raises:
+        Exception: If model loading fails.
+    """
+    global models_cache, current_model_selection
+
+    model_key = model_var.value
+
+    # Check if model is already loaded
+    if model_key in models_cache:
+        current_model_selection = model_var
+        return models_cache[model_key]
+
+    print(f'Loading {model_var.value} model...')
+
+    try:
+        model_selection = config.architecture
+        model = get_model(config.architecture, config)
+
+        # Cache the loaded model and processor
+        models_cache[model_key] = model
+        current_model_selection = model_selection
+
+        print(f'{model_selection.value} model loaded successfully!')
+        return model
+
+    except Exception as e:
+        print(f'Error loading model {model_selection.value}: {str(e)}')
+        raise
+
+
+def get_single_image_probabilities(
+    instruction: str,
+    image: Image.Image,
+    vlm: ModelBase,
+    model_selection: ModelSelection,
+    top_k: int = 8
+) -> Tuple[List[str], np.ndarray]:
+    """Process a single image and return first token probabilities.
+
+    Args:
+        instruction: Text instruction for the model.
+        image: PIL Image to process.
+        vlm: Loaded model.
+        model_selection: The VLM being used.
+        top_k: Number of top tokens to return.
+
+    Returns:
+        Tuple containing list of top tokens and their probabilities.
+    """
+    # Generate prompt and process inputs
+    text = vlm._generate_prompt(instruction, has_images=True)
+    inputs = vlm._generate_processor_output(text, image)
+
+    with torch.no_grad():
+        outputs = vlm.model.generate(
+            **inputs,
+            max_new_tokens=1,  # Only generate first token
+            output_scores=True,
+            return_dict_in_generate=True,
+            do_sample=False
+        )
+
+    # Get the logits for the first generated token
+    first_token_logits = outputs.scores[0][0]  # Shape: [vocab_size]
+
+    # Convert logits to probabilities
+    probabilities = torch.softmax(first_token_logits, dim=-1)
+
+    # Get top-k probabilities for visualization
+    top_probs, top_indices = torch.topk(probabilities, top_k)
+
+    # Convert tokens back to text
+    top_tokens = [vlm.processor.tokenizer.decode([idx.item()]) for idx in top_indices]
+
+    return top_tokens, top_probs.cpu().numpy()
+
+
+def scale_figure_fonts(fig: Figure, factor: float = 1.5) -> None:
+    """Multiply all text sizes in a Matplotlib Figure by `factor`.
+
+    Args:
+        fig: The Matplotlib Figure to scale.
+        factor: The scaling factor (e.g., 1.5 to increase by 50%).
+    """
+    for ax in fig.get_axes():
+        # titles & axis labels
+        ax.title.set_fontsize(ax.title.get_fontsize() * factor)
+        ax.xaxis.label.set_size(ax.xaxis.label.get_size() * factor)
+        ax.yaxis.label.set_size(ax.yaxis.label.get_size() * factor)
+        # tick labels
+        for lbl in ax.get_xticklabels() + ax.get_yticklabels():
+            lbl.set_fontsize(lbl.get_fontsize() * factor)
+        # texts placed via ax.text(...) (e.g., numbers above bars / "No data" notes)
+        for t in ax.texts:
+            t.set_fontsize(t.get_fontsize() * factor)
+    # any stray Text artists attached to the figure (rare, but safe)
+    for t in fig.findobj(match=Text):
+        if t.figure is fig:
+            t.set_fontsize(t.get_fontsize() * factor)
+
+
+def create_dual_probability_plot(
+    tokens1: List[str], probabilities1: np.ndarray,
+    tokens2: List[str], probabilities2: np.ndarray
+) -> Figure:
+    """Create a matplotlib plot comparing token probabilities from two images.
+
+    Args:
+        tokens1: List of token strings from first image.
+        probabilities1: Array of probability values from first image.
+        tokens2: List of token strings from second image.
+        probabilities2: Array of probability values from second image.
+
+    Returns:
+        Matplotlib Figure object.
+    """
+    if len(tokens1) == 0 and len(tokens2) == 0:
+        fig, ax = plt.subplots(figsize=(15, 8))
+        ax.text(0.5, 0.5, 'No data to display',
+                horizontalalignment='center', verticalalignment='center')
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        return fig
+
+    # Unify y-range with padding (cap at 1.0)
+    max1 = float(np.max(probabilities1)) if len(tokens1) else 0.0
+    max2 = float(np.max(probabilities2)) if len(tokens2) else 0.0
+    y_upper = min(1.0, max(max1, max2) * 1.15 + 1e-6)  # ~15% headroom
+
+    # Create subplots side by side with shared y
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 12), sharey=True)
+    ax1.set_ylim(0, y_upper)
+    ax2.set_ylim(0, y_upper)
+
+    # Plot first image results
+    if len(tokens1) > 0:
+        bars1 = ax1.bar(range(len(tokens1)), probabilities1, color='lightcoral',
+                        edgecolor='darkred', alpha=0.7)
+        ax1.set_xlabel('Tokens', fontsize=12)
+        ax1.set_ylabel('Probability', fontsize=12)
+        ax1.set_title('Image 1 - First Token Probabilities',
+                      fontsize=14, fontweight='bold')
+        ax1.set_xticks(range(len(tokens1)))
+        ax1.set_xticklabels(tokens1, rotation=45, ha='right')
+
+        # Clamp label position so it stays inside the axes
+        for bar, prob in zip(bars1, probabilities1):
+            h = bar.get_height()
+            y = min(h + 0.02 * y_upper, y_upper * 0.98)
+            ax1.text(bar.get_x() + bar.get_width()/2., y, f'{prob:.3f}',
+                     ha='center', va='bottom', fontsize=9)
+
+        ax1.grid(axis='y', alpha=0.3)
+    else:
+        ax1.text(0.5, 0.5, 'No data for Image 1',
+                 horizontalalignment='center', verticalalignment='center')
+        ax1.set_xlim(0, 1)
+        ax1.set_ylim(0, 1)
+
+    # Plot second image results
+    if len(tokens2) > 0:
+        bars2 = ax2.bar(range(len(tokens2)), probabilities2, color='skyblue',
+                        edgecolor='navy', alpha=0.7)
+        ax2.set_xlabel('Tokens', fontsize=12)
+        ax2.set_ylabel('Probability', fontsize=12)
+        ax2.set_title('Image 2 - First Token Probabilities',
+                      fontsize=14, fontweight='bold')
+        ax2.set_xticks(range(len(tokens2)))
+        ax2.set_xticklabels(tokens2, rotation=45, ha='right')
+
+        for bar, prob in zip(bars2, probabilities2):
+            h = bar.get_height()
+            y = min(h + 0.02 * y_upper, y_upper * 0.98)
+            ax2.text(bar.get_x() + bar.get_width()/2., y, f'{prob:.3f}',
+                     ha='center', va='bottom', fontsize=9)
+
+        ax2.grid(axis='y', alpha=0.3)
+    else:
+        ax2.text(0.5, 0.5, 'No data for Image 2',
+                 horizontalalignment='center', verticalalignment='center')
+        ax2.set_xlim(0, 1)
+        ax2.set_ylim(0, 1)
+
+    # Give extra space for rotated tick labels
+    fig.tight_layout()
+    fig.subplots_adjust(bottom=0.18)
+
+    return fig
+
+
+def get_module_similarity_pooled(
+        vlm: ModelBase,
+        module_name: str,
+        image1: Image.Image,
+        image2: Image.Image,
+        instruction: str,
+        pooling: str = 'mean'
+) -> float:
+    """Compute cosine similarity with optional pooling strategies.
+
+    Args:
+        vlm: The loaded VLM (ModelBase instance).
+        module_name: The layer/module name to extract features from.
+        image1: First PIL Image.
+        image2: Second PIL Image.
+        instruction: Text instruction for the model.
+        pooling: Pooling strategy - 'mean', 'max', 'cls', or 'none'.
+
+    Returns:
+        Cosine similarity value between the two embeddings.
+
+    Raises:
+        ValueError: If feature extraction fails or module not found.
+    """
+    embeddings = {}
+    target_module = None
+
+    def hook_fn(
+        module: torch.nn.Module,
+        input: Any,
+        output: Any
+    ) -> None:
+        """Forward hook to capture module output.
+
+        Args:
+            module: The module being hooked.
+            input: The input to the module.
+            output: The output from the module.
+        """
+        if isinstance(output, tuple):
+            embeddings['activation'] = output[0].detach()
+        else:
+            embeddings['activation'] = output.detach()
+
+    # Find and register hook
+    for name, module in vlm.model.named_modules():
+        if name == module_name:
+            target_module = module
+            hook_handle = module.register_forward_hook(hook_fn)
+            break
+
+    if target_module is None:
+        raise ValueError(f"Module '{module_name}' not found in model")
+
+    try:
+        # Extract embedding for image1
+        text = vlm._generate_prompt(instruction, has_images=True)
+        inputs1 = vlm._generate_processor_output(text, image1)
+
+        embeddings.clear()
+        with torch.no_grad():
+            _ = vlm.model(**inputs1)
+
+        if 'activation' not in embeddings:
+            raise ValueError('Failed to extract features for image1')
+
+        embedding1 = embeddings['activation']
+
+        # Extract embedding for image2
+        inputs2 = vlm._generate_processor_output(text, image2)
+
+        embeddings.clear()
+        with torch.no_grad():
+            _ = vlm.model(**inputs2)
+
+        if 'activation' not in embeddings:
+            raise ValueError('Failed to extract features for image2')
+
+        embedding2 = embeddings['activation']
+
+        # Apply pooling strategy
+        if pooling == 'mean':
+            # Mean pooling across sequence dimension
+            if embedding1.dim() >= 2:
+                embedding1_pooled = embedding1.mean(dim=1)
+                embedding2_pooled = embedding2.mean(dim=1)
+            else:
+                embedding1_pooled = embedding1
+                embedding2_pooled = embedding2
+
+        elif pooling == 'max':
+            # Max pooling across sequence dimension
+            if embedding1.dim() >= 2:
+                embedding1_pooled = embedding1.max(dim=1)[0]
+                embedding2_pooled = embedding2.max(dim=1)[0]
+            else:
+                embedding1_pooled = embedding1
+                embedding2_pooled = embedding2
+
+        elif pooling == 'cls':
+            # Use first token (CLS token)
+            if embedding1.dim() >= 2:
+                embedding1_pooled = embedding1[:, 0, :]
+                embedding2_pooled = embedding2[:, 0, :]
+            else:
+                embedding1_pooled = embedding1
+                embedding2_pooled = embedding2
+
+        elif pooling == 'none':
+            # Flatten without pooling
+            embedding1_pooled = embedding1.reshape(embedding1.shape[0], -1)
+            embedding2_pooled = embedding2.reshape(embedding2.shape[0], -1)
+        else:
+            raise ValueError(f'Unknown pooling strategy: {pooling}')
+
+        # Ensure 2D shape [batch, features]
+        if embedding1_pooled.dim() == 1:
+            embedding1_pooled = embedding1_pooled.unsqueeze(0)
+            embedding2_pooled = embedding2_pooled.unsqueeze(0)
+
+        # Compute cosine similarity
+        similarity = F.cosine_similarity(embedding1_pooled, embedding2_pooled, dim=1)
+        similarity_value = float(similarity.mean().cpu().item())
+
+        return similarity_value
+
+    finally:
+        hook_handle.remove()
+
+
+def process_dual_inputs(
+    model_choice: str,
+    selected_layer: str,
+    instruction: str,
+    image1: Optional[Image.Image],
+    image2: Optional[Image.Image],
+    top_k: int = 8
+) -> Tuple[Optional[Figure], str]:
+    """Main function to process dual inputs and return comparison plot.
+
+    Args:
+        model_choice: String name of the selected model.
+        selected_layer: String name of the selected layer.
+        instruction: Text instruction for the model.
+        image1: First PIL Image to process, can be None.
+        image2: Second PIL Image to process, can be None.
+        top_k: Number of top tokens to display.
+
+    Returns:
+        Tuple containing the plot figure and info text.
+    """
+    if image1 is None and image2 is None:
+        return None, 'Please upload at least one image.'
+
+    if not instruction.strip():
+        return None, 'Please provide an instruction.'
+
+    if not model_choice:
+        return None, 'Please select a model.'
+
+    if not selected_layer:
+        return None, 'Please select a layer.'
+
+    try:
+        # Initialize a config
+        model_var = ModelVariants(model_choice.lower())
+        model_selection, model_path, _ = get_model_info(model_var)
+        config = Config(model_selection, model_path, selected_layer, instruction)
+        config.model = {
+            'torch_dtype': torch.float16,
+            'low_cpu_mem_usage': True,
+            'device_map': 'auto'
+        }
+
+        # Load the model
+        model = load_model(model_var, config)
+
+        # Handle cases where only one image is provided
+        if image1 is None:
+            image1 = image2
+            tokens1, probs1 = [], np.array([])
+            tokens2, probs2 = get_single_image_probabilities(
+                instruction, image2, model, model_selection, top_k
+            )
+        elif image2 is None:
+            image2 = image1
+            tokens1, probs1 = get_single_image_probabilities(
+                instruction, image1, model, model_selection, top_k
+            )
+            tokens2, probs2 = [], np.array([])
+        else:
+            tokens1, probs1 = get_single_image_probabilities(
+                instruction, image1, model, model_selection, top_k
+            )
+            tokens2, probs2 = get_single_image_probabilities(
+                instruction, image2, model, model_selection, top_k
+            )
+
+        if len(tokens1) == 0 and len(tokens2) == 0:
+            return None, 'Error: Could not process the inputs. Please check the model loading.'
+
+        # Create comparison plot
+        plot = create_dual_probability_plot(
+            tokens1, probs1, tokens2, probs2
+        )
+        scale_figure_fonts(plot, factor=1.25)
+
+        # Create info text
+        info_text = f'Model: {model_choice.upper()}\n'
+        info_text += f'Top-K: {top_k}\n'
+        info_text += f"Instruction: '{instruction}'\n\n"
+
+        if len(tokens1) > 0:
+            info_text += f"Image 1 - Top token: '{tokens1[0]}' (probability: {probs1[0]:.4f})\n"
+        else:
+            info_text += 'Image 1 - No data\n'
+
+        if len(tokens2) > 0:
+            info_text += f"Image 2 - Top token: '{tokens2[0]}' (probability: {probs2[0]:.4f})\n"
+        else:
+            info_text += 'Image 2 - No data\n'
+
+        if len(tokens1) > 0 and len(tokens2) > 0:
+            info_text += f'\nLayer: {selected_layer}\n'
+            similarity = get_module_similarity_pooled(model, selected_layer, image1, image2, instruction)
+            info_text += f'Cosine similarity between Image 1 and 2: {similarity:.3f}\n'
+
+        return plot, info_text
+
+    except ValueError as e:
+        return None, f'Invalid model selection: {str(e)}'
+    except Exception as e:
+        return None, f'Error: {str(e)}'
+
+
+def create_demo() -> gr.Blocks:
+    """Create and configure the Gradio demo interface for dual image comparison.
+
+    Returns:
+        Configured Gradio Blocks interface.
+    """
+    with gr.Blocks(title='VLM-Lens Visualizer') as demo:
+        gr.Markdown("""
+        # VLM-Lens Demo
+
+        This VLM-Lens demo processes an instruction with up to two images through various Vision-Language Models (VLMs)
+        and visualizes the probability distribution of the first token in the response for each image.
+
+        **Instructions:**
+        1. Select a VLM from the dropdown
+        2. Select a layer from the available embedding layers
+        3. Upload two images for comparison
+        4. Enter your instruction/question about the images
+        5. Adjust the number of top tokens to display (1-20)
+        6. Click "Analyze" to see the first token probability distributions side by side
+
+        **Note:** You can upload just one image if you prefer single image analysis.
+        """)
+
+        with gr.Row():
+            with gr.Column():
+                model_dropdown = gr.Dropdown(
+                    choices=[v.value.capitalize() for v in ModelVariants],
+                    label='Select VLM',
+                    value=None,
+                    interactive=True
+                )
+
+                layer_dropdown = gr.Dropdown(
+                    choices=[],
+                    label='Select Module',
+                    visible=False,
+                    interactive=True
+                )
+
+                instruction_input = gr.Textbox(
+                    label='Instruction',
+                    placeholder='Describe what you see in this image...',
+                    lines=3
+                )
+
+                top_k_slider = gr.Slider(
+                    minimum=1,
+                    maximum=20,
+                    value=8,
+                    step=1,
+                    label='Number of Top Tokens to Display',
+                    info='Select how many top probability tokens to show in the visualization'
+                )
+
+                with gr.Row():
+                    image1_input = gr.Image(
+                        label='Upload Image 1',
+                        type='pil'
+                    )
+                    image2_input = gr.Image(
+                        label='Upload Image 2',
+                        type='pil'
+                    )
+
+                analyze_btn = gr.Button('Analyze', variant='primary', visible=False)
+
+            with gr.Column():
+                plot_output = gr.Plot(label='First Token Probability Distribution Comparison')
+                info_output = gr.Textbox(
+                    label='Analysis Info',
+                    lines=8,
+                    interactive=False
+                )
+
+        # Set up event handlers
+        model_dropdown.change(
+            fn=update_layer_choices,
+            inputs=[model_dropdown],
+            outputs=[layer_dropdown, analyze_btn]
+        )
+
+        analyze_btn.click(
+            fn=process_dual_inputs,
+            inputs=[model_dropdown, layer_dropdown, instruction_input, image1_input, image2_input, top_k_slider],
+            outputs=[plot_output, info_output]
+        )
+
+        # Add examples
+        gr.Examples(
+            examples=[
+                ['What is in this image? Describe in one word.', None, None],
+                ['Describe the main object in the picture in one word.', None, None],
+                ['What color is the dominant object? Describe in one word.', None, None],
+            ],
+            inputs=[instruction_input, image1_input, image2_input]
+        )
+
+    return demo
+
+
+if __name__ == '__main__':
+    # Create and launch the demo
+    demo = create_demo()
+    demo.launch(
+        share=True,
+        server_name='0.0.0.0',
+        server_port=7860
+    )

demo/lookup.py

@@ -0,0 +1,171 @@
+"""Model info lookup utilities."""
+
+import os
+from enum import Enum
+from pathlib import Path
+from typing import Tuple
+
+from src.models.config import ModelSelection
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+SPECS_DIR = Path(os.getenv('MODEL_SPECS_DIR', REPO_ROOT / 'logs'))
+
+# TODO: To store local model weights in the repo, also define:
+# MODELS_DIR = Path(os.getenv('MODELS_DIR', REPO_ROOT / 'checkpoints'))
+
+
+class ModelVariants(str, Enum):
+    """Enum that contains all possible model variants."""
+    AYA_VISION_8B = 'aya-vision-8b'
+    BLIP2_3B = 'blip2-opt-2.7b'
+    COGVLM_17B = 'cogvlm-17b'
+    GLAMM_7B = 'glamm-7b'
+    INTERNLM_XC_25_7B = 'internlm-xcomposer2.5-7b'
+    INTERNVL_25_8B = 'internvl-2.5-8b'
+    JANUS_1B = 'janus-pro-1b'
+    LLAVA_15_7B = 'llava-1.5-7b'
+    MINICPM_O_26_8B = 'minicpm-o-2.6-8b'
+    MINICPM_V_20_3B = 'minicpm-v-2.0-2.8b'
+    MOLMO_7B = 'molmo-7b'
+    PALIGEMMA_3B = 'paligemma-3b'
+    PIXTRAL_12B = 'pixtral-12b'
+    PERCEPTION_LM_1B = 'perception-lm-1b'
+    QWENVL_20_2B = 'qwen2-vl-2b-instruct'
+    QWENVL_20_7B = 'qwen2-vl-7b-instruct'
+    # TODO: Add more models here as needed.
+
+
+# ---- Mapping ----
+# model_path: can be a local path or a HF repo id string
+# model_spec: absolute Path to the .txt file (we'll return a repo-root-relative string)
+_MODEL_MAPPING: dict[ModelVariants, dict[ModelSelection, str, str | Path]] = {
+    ModelVariants.AYA_VISION_8B: {
+        'model_arch': ModelSelection.AYA_VISION,
+        'model_path': 'CohereLabs/aya-vision-8b',
+        'model_spec': SPECS_DIR / 'CohereLabs' / 'aya-vision-8b.txt',
+    },
+    ModelVariants.BLIP2_3B: {
+        'model_arch': ModelSelection.BLIP2,
+        'model_path': 'Salesforce/blip2-opt-2.7b',
+        'model_spec': SPECS_DIR / 'Salesforce' / 'blip2-opt-2.7b.txt',
+    },
+    ModelVariants.COGVLM_17B: {
+        'model_arch': ModelSelection.COGVLM,
+        'model_path': 'THUDM/cogvlm-chat-hf',
+        'model_spec': SPECS_DIR / 'THUDM' / 'cogvlm-chat-hf.txt',
+    },
+    ModelVariants.GLAMM_7B: {
+        'model_arch': ModelSelection.GLAMM,
+        'model_path': 'MBZUAI/GLaMM-FullScope',
+        'model_spec': SPECS_DIR / 'MBZUAI' / 'GLaMM-FullScope.txt',
+    },
+    ModelVariants.INTERNLM_XC_25_7B: {
+        'model_arch': ModelSelection.INTERNLM_XC,
+        'model_path': 'internlm/internlm-xcomposer2d5-7b',
+        'model_spec': SPECS_DIR / 'internlm' / 'internlm-xcomposer2d5-7b.txt',
+    },
+    ModelVariants.INTERNVL_25_8B: {
+        'model_arch': ModelSelection.INTERNVL,
+        'model_path': 'OpenGVLab/InternVL2_5-8B',
+        'model_spec': SPECS_DIR / 'internvl' / 'InternVL2_5-8B.txt',
+    },
+    ModelVariants.JANUS_1B: {
+        'model_arch': ModelSelection.JANUS,
+        'model_path': 'deepseek-community/Janus-Pro-1B',
+        'model_spec': SPECS_DIR / 'deepseek-community' / 'Janus-Pro-1B.txt',
+    },
+    ModelVariants.LLAVA_15_7B: {
+        'model_arch': ModelSelection.LLAVA,
+        'model_path': 'llava-hf/llava-1.5-7b-hf',
+        'model_spec': SPECS_DIR / 'llava-hf' / 'llava-1.5-7b-hf.txt',
+    },
+    ModelVariants.MINICPM_O_26_8B: {
+        'model_arch': ModelSelection.MINICPM,
+        'model_path': 'openbmb/MiniCPM-o-2_6',
+        'model_spec': SPECS_DIR / 'openbmb' / 'MiniCPM-o-2_6.txt',
+    },
+    ModelVariants.MINICPM_V_20_3B: {
+        'model_arch': ModelSelection.MINICPM,
+        'model_path': 'compling/MiniCPM-V-2',
+        'model_spec': SPECS_DIR / 'wonderwind271' / 'MiniCPM-V-2.txt',
+    },
+    ModelVariants.MOLMO_7B: {
+        'model_arch': ModelSelection.MOLMO,
+        'model_path': 'allenai/Molmo-7B-D-0924',
+        'model_spec': SPECS_DIR / 'allenai' / 'Molmo-7B-D-0924.txt',
+    },
+    ModelVariants.PALIGEMMA_3B: {
+        'model_arch': ModelSelection.PALIGEMMA,
+        'model_path': 'google/paligemma-3b-mix-224',
+        'model_spec': SPECS_DIR / 'paligemma' / 'paligemma-3b.txt',
+    },
+    ModelVariants.PIXTRAL_12B: {
+        'model_arch': ModelSelection.PIXTRAL,
+        'model_path': 'mistralai/Pixtral-12B-2409',
+        'model_spec': SPECS_DIR / 'mistralai' / 'Pixtral-12B-2409.txt',
+    },
+    ModelVariants.PERCEPTION_LM_1B: {
+        'model_arch': ModelSelection.PLM,
+        'model_path': 'facebook/Perception-LM-1B',
+        'model_spec': SPECS_DIR / 'facebook' / 'Perception-LM-1B.txt',
+    },
+    ModelVariants.QWENVL_20_2B: {
+        'model_arch': ModelSelection.QWEN,
+        'model_path': 'Qwen/Qwen2-VL-2B-Instruct',
+        'model_spec': SPECS_DIR / 'Qwen' / 'Qwen2-VL-2B-Instruct.txt',
+    },
+    ModelVariants.QWENVL_20_7B: {
+        'model_arch': ModelSelection.QWEN,
+        'model_path': 'Qwen/Qwen2-VL-7B-Instruct',
+        'model_spec': SPECS_DIR / 'Qwen' / 'Qwen2-VL-7B-Instruct.txt',
+    },
+    # TODO: Add more models here as needed.
+}
+
+
+def _to_repo_relative(p: Path) -> str:
+    """Convert a path to a repo-root–relative string if possible.
+
+    Args:
+        p (Path): The path to convert.
+
+    Returns:
+        str: `p` relative to ``REPO_ROOT`` if `p` is within it; otherwise the
+            absolute path as a string.
+    """
+    try:
+        return str(p.resolve().relative_to(REPO_ROOT))
+    except ValueError:
+        return str(p)
+
+
+def get_model_info(model_var: ModelVariants) -> Tuple[ModelSelection, str, str]:
+    """Return the model path and spec link for a given selection.
+
+    Args:
+        model_var (ModelVariants): The model variant to look up.
+
+    Returns:
+        Tuple[ModelSelection, str, str]:
+            A triple of ``(model_selection, model_path, link_to_model_spec)`` where
+            `model_selection` is a ModelSelection enum entry,
+            `model_path` is an HF repo id or local path, and
+            `link_to_model_spec` is a repo-root-relative path to the spec ``.txt``.
+
+    Raises:
+        KeyError: If the provided `model` is unknown / not in the mapping.
+        FileNotFoundError: If the resolved spec file does not exist.
+    """
+    try:
+        info = _MODEL_MAPPING[model_var]
+    except KeyError as e:
+        raise KeyError(f'Unknown model: {model_var!r}') from e
+
+    model_selection = ModelSelection(info['model_arch'])
+    model_path = str(info['model_path'])
+    spec_path = Path(info['model_spec']).resolve()
+
+    if not spec_path.exists():
+        raise FileNotFoundError(f'Spec file not found: {spec_path}')
+
+    return model_selection, model_path, _to_repo_relative(spec_path)

demo/requirements.txt

@@ -0,0 +1,2 @@
+gradio==5.47.2
+accelerate==1.10.1

logs/CohereLabs/aya-vision-8b.txt

@@ -0,0 +1,729 @@
+
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