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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- ibm-granite/granite-vision-3.3-2b-preview |
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library_name: transformers |
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--- |
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# granite-vision-embedding-3.3-2b |
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**Model Summary:** |
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granite-vision-embedding-3.3-2b is an efficient embedding model, based on granite-vision Vision Language Model(VLM). This model is specifically designed for multi-modal document retrieval, enabling queries on documents with tables, charts, infographics, and complex layout. The model generates ColBERT-style multi-vector representations of pages. |
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The model eliminates the need for OCR-based text extraction and related preprocessing steps. |
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**Evaluations:** |
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We evaluated granite-vision-embedding-3.3-2b alongside other top colBERT style multi-modal embedding models in the 1B-3B parameter range using two benchmark: Vidore2 and [Real-MM-RAG-Bench](https://arxiv.org/abs/2502.12342) which are specifically addressing complex multi-modal documents retrieval task. |
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## **NDCG@5 - ViDoRe V2** |
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| Collection \ Model | ColPali-v1.3 | ColQwen2.5-v0.2 | ColNomic-3b | ColGraniteVision-3.3-2b | |
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|----------------------------------------|--------------|------------------|-------------|--------------------------| |
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| ESG Restaurant Human | 51.10 | 68.40 | 65.80 | 60.00 | |
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| Economics Macro Multilingual | 49.90 | 56.50 | 55.40 | 50.13 | |
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| MIT Biomedical | 59.70 | 63.60 | 63.50 | 60.00 | |
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| ESG Restaurant Synthetic | 57.00 | 57.40 | 56.60 | 54.00 | |
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| ESG Restaurant Synthetic Multilingual | 55.70 | 57.40 | 57.20 | 52.00 | |
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| MIT Biomedical Multilingual | 56.50 | 61.10 | 62.50 | 54.00 | |
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| Economics Macro | 51.60 | 59.80 | 60.20 | 57.00 | |
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| **Avg (ViDoRe2)** | **54.50** | **60.60** | **60.17** | **55.30** | |
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## **NDCG@5 - REAL-MM-RAG** |
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| Collection \ Model | ColPali-v1.3 | ColQwen2.5-v0.2 | ColNomic-3b | ColGraniteVision-3.3-2b | |
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|----------------------------------------|--------------|------------------|-------------|--------------------------| |
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| FinReport | 0.55 | 0.66 | 0.78 | 0.60 | |
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| FinSlides | 0.68 | 0.79 | 0.81 | 0.72 | |
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| TechReport | 0.78 | 0.86 | 0.88 | 0.80 | |
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| TechSlides | 0.90 | 0.93 | 0.92 | 0.92 | |
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| **Avg (REAL-MM-RAG)** | **0.73** | **0.81** | **0.85** | **0.76** | |
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- **Release Date**: June 2025 |
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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**Supported Input Format:** |
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Currently the model supports English queries and images (png, jpeg, etc.) as input format. |
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**Intended Use:** |
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The model is intended to be used in enterprise applications that involve retrieval of visual and text data. In particular, the model is well-suited for multi-modal RAG systems where the knowledge base is composed of complex enterprise documents, such as reports, slides, images, canned doscuments, manuals and more. The model can be used as a standalone retriever, or alongside a text-based retriever. |
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### Usage |
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First, make sure to build the latest verions of transormers: |
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```shell |
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pip install -q torch torchvision torchaudio |
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pip install transformers>=4.49 |
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``` |
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Then run the code: |
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```python |
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from transformers import AutoProcessor, AutoModel |
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from PIL import Image |
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import torch |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model_name = "ibm-granite/granite-vision-embedding-3.3-2b" |
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model = AutoModel.from_pretrained(model_name, trust_remote_code=True,torch_dtype=torch.float16).to(device).eval() |
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processor = AutoProcessor.from_pretrained(model_name,trust_remote_code=True) |
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# ───────────────────────────────────────────── |
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# Inputs: Image + Text |
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# ───────────────────────────────────────────── |
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image_url = "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg" |
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print("\nFetching image...") |
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image = Image.open(BytesIO(requests.get(image_url).content)).convert("RGB") |
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text = "A photo of a tiger" |
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print(f"Image and text inputs ready.") |
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# Process both inputs |
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print("Processing inputs...") |
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image_inputs = processor.process_images([image]) |
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text_inputs = processor.process_queries([text]) |
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# Move to correct device |
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image_inputs = {k: v.to(device) for k, v in image_inputs.items()} |
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text_inputs = {k: v.to(device) for k, v in text_inputs.items()} |
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# ───────────────────────────────────────────── |
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# Run Inference |
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# ───────────────────────────────────────────── |
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with torch.no_grad(): |
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print("🔍 Getting image embedding...") |
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img_emb = model(**image_inputs) |
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print("✍️ Getting text embedding...") |
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txt_emb = model(**text_inputs) |
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# ───────────────────────────────────────────── |
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# Score the similarity |
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# ───────────────────────────────────────────── |
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print("Scoring similarity...") |
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similarity = processor.score(txt_emb, img_emb, batch_size=1, device=device) |
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print("\n" + "=" * 50) |
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print(f"📊 Similarity between image and text: {similarity.item():.4f}") |
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print("=" * 50) |
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``` |
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### Use granite-vision-embedding-3.3-2b for MM RAG |
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For an example of MM RAG using col-granite-visionrefer to [this notebook](......). |
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**Model Architecture:** |
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We built our model upon [granite-vision-3.3-2b](https://huggingface.co/ibm-granite/granite-vision-3.3-2b) with additional projection layer. |
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**Training Data:** |
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The model was trained on a random subset from DOCFM. DOCFM is a large-scale comprehensive dataset effort at IBM consisting of 85 million document pages extracted from unique PDF |
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documents sourced from Common Crawl, Wikipedia, and ESG (Environmental, Social, and Governance) |
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reports. For each image in the dataset, Pseudo-questions were generated using Pixtral12B VLM. |
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**Infrastructure:** |
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We train granite-vision-embedding-3.3-2b on IBM’s cognitive computing cluster, which is outfitted with NVIDIA A100 GPUs. |
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**Ethical Considerations and Limitations:** |
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The use of Large Vision and Language Models involves risks and ethical considerations people must be aware of, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. col-granite-vision-1.0-2b is not the exception in this regard. Although our alignment processes include safety considerations, the model may in some cases produce inaccurate or biased responses. |
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Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use col-granite-vision-1.0-2b with ethical intentions and in a responsible way. |
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**Resources** |
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- :page_facing_up: Granite Vision technical report [here](https://arxiv.org/abs/2502.09927) |
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- :star:️ Learn about the latest updates with Granite: https://www.ibm.com/granite |
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- :rocket: Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/ |
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- :bulb: Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources |
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