File size: 14,343 Bytes
5b445c5 dce7388 5b445c5 dce7388 dc4f170 dce7388 dc4f170 dce7388 5b445c5 dce7388 dc4f170 dce7388 dc4f170 5b445c5 dce7388 5b445c5 dce7388 5b445c5 dce7388 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 | ---
pipeline_tag: visual-document-retrieval
library_name: transformers
language:
- multilingual
license: other
license_name: webai-non-commercial-license-v1.0
license_link: https://huggingface.co/webAI-Official/webAI-ColVec1-4b/blob/main/LICENSE.md
base_model: Qwen/Qwen3.5-4B
tags:
- text
- image
- video
- multimodal-embedding
- vidore
- colpali
- colqwen3_5
- multilingual-embedding
---
# webAI-Official/webAI-ColVec1-4b
## ⚡ Summary
**webAI-Official/webAI-ColVec1-4b** is a state-of-the-art [ColBERT](https://arxiv.org/abs/2407.01449)-style multimodal embedding model based on *[Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B)*. It maps text queries, visual documents (images, PDFs) into aligned multi-vector embeddings.
The model has been fine-tuned on a **merged multimodal dataset** of ~2M question-image pairs, including [DocVQA](https://huggingface.co/datasets/lmms-lab/DocVQA), [PubTables-1M](https://huggingface.co/datasets/bsmock/pubtables-1m), [TAT-QA](https://huggingface.co/datasets/next-tat/TAT-QA), [ViDoRe-ColPali-Training](https://huggingface.co/datasets/vidore/colpali_train_set), [VDR Multilingual](https://huggingface.co/datasets/llamaindex/vdr-multilingual-train), [VisRAG-Ret-Train-In-domain-data](https://huggingface.co/datasets/openbmb/VisRAG-Ret-Train-In-domain-data), [VisRAG-Ret-Train-Synthetic-data](https://huggingface.co/datasets/openbmb/VisRAG-Ret-Train-Synthetic-data) and proprietary domain-specific synthetic data
The datasets were filtered, balanced, and merged to produce a comprehensive training set optimized for multilingual, multimodal retrieval and document-image understanding. The model achieves **competitive performance across ViDoRe V1 & V3** (English and multilingual).
## 🛠️ Model Specifications
| Feature | Detail |
| --------------------- | ------------------------------------------------------------------------- |
| **Architecture** | Qwen3.5-4B Vision-Language Model (VLM) + `640 dim` Linear Projection Head |
| **Methodology** | ColBERT-style Late Interaction (MaxSim scoring) |
| **Output** | Multi-vector (Seq_Len × *640*), L2-normalized |
| **Modalities** | Text Queries, Images (Documents) |
| **Training Strategy** | LoRA adapters + Fully-trained projection layer |
| **Precision** | `bfloat16` weights, FlashAttention 2 enabled |
---
### Key Properties
- **Unified Encoder (Single-Tower):** A single shared language model processes both images and text. Images are converted into visual tokens via a vision encoder and injected into the token stream, no separate dual encoders.
- **Projection Head:** A single linear layer projects final hidden states → compact embedding space (*hidden_size → 640 dim*).
- No activation
- Fully trained
- Replaces LM head for retrieval
- **Multi-Vector Representation:** Each token becomes an embedding → enables fine-grained token-level matching instead of single-vector pooling.
## 📊 Evaluation Results
We report results on the **ViDoRe** benchmark suite. The tables below summarize the image-modality accuracy of `webAI-ColVec1-4b` on the ViDoRe V1 and V3 benchmarks, alongside other webAI `ColVec1` models. Note that (M)MTEB leaderboards use Borda ranking. Each task acts like a voter that ranks models based on how well they perform. Models earn more points when they rank higher on a task. The model with the most total points across all tasks gets the top overall rank.
### ViDoRe V3 (NDCG@10)
| Model | CompSci | Energy | FinanceEn | FinanceFr | HR | Industrial | Pharma | Physics | **Avg (Public)** |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **[webAI-ColVec1-9b](https://huggingface.co/webAI-Official/webAI-ColVec1-9b)** | **0.8092** | 0.6976 | 0.6827 | **0.5372** | **0.7004** | **0.5718** | **0.6732** | 0.4838 | **0.6445** |
| [nemotron-colembed-vl-8b-v2](https://huggingface.co/nvidia/nemotron-colembed-vl-8b-v2) | 0.7929 | **0.6982** | 0.6729 | 0.5154 | 0.6632 | 0.5603 | 0.6719 | **0.5084** | 0.6354 |
| **[webAI-ColVec1-4b](https://huggingface.co/webAI-Official/webAI-ColVec1-4b)** | 0.7983 | 0.6869 | **0.6848** | 0.5111 | 0.6739 | 0.5573 | 0.6567 | 0.5014 | 0.6338 |
| [tomoro-colqwen3-embed-8b](https://huggingface.co/TomoroAI/tomoro-colqwen3-embed-8b) | 0.7535 | 0.6841 | 0.6508 | 0.4910 | 0.6398 | 0.5441 | 0.6636 | 0.5013 | 0.6160 |
| [colqwen3.5-4.5B-v3](https://huggingface.co/athrael-soju/colqwen3.5-4.5B-v3) | 0.7866 | 0.6804 | 0.6406 | 0.4856 | 0.6206 | 0.5520 | 0.6559 | 0.5034 | 0.6156 |
### ViDoRe V1 (NDCG@5)
| Model | ArxivQA | DocVQA | InfoVQA | Shift | Syn-AI | Syn-Eng | Syn-Gov | Syn-Health | TabFQuAD | Tatdqa | **Avg** |
| :------------------------------------------------------------------------------------------------- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- |
| [nemotron-colembed-vl-8b-v2](https://huggingface.co/nvidia/nemotron-colembed-vl-8b-v2) | 0.9310 | 0.6810 | 0.9460 | **0.9330** | **1.0000** | 0.9790 | **0.9890** | 0.9960 | 0.9770 | 0.8340 | **0.9270** |
| [llama-nemotron-colembed-vl-3b-v2](https://huggingface.co/nvidia/llama-nemotron-colembed-vl-3b-v2) | 0.9040 | 0.6720 | 0.9470 | 0.9200 | **1.0000** | **0.9800** | 0.9800 | 0.9890 | 0.9730 | 0.8100 | 0.9170 |
| [nemotron-colembed-vl-4b-v2](https://huggingface.co/nvidia/nemotron-colembed-vl-4b-v2) | 0.9200 | 0.6740 | 0.9330 | 0.9230 | 0.9930 | 0.9620 | 0.9800 | 0.9850 | **0.9810** | 0.8120 | 0.9160 |
| [colqwen3.5-4.5B-v3](https://huggingface.co/athrael-soju/colqwen3.5-4.5B-v3) | 0.9190 | 0.6660 | 0.9360 | 0.9020 | **1.0000** | 0.9710 | 0.9730 | 0.9890 | 0.9590 | **0.8400** | 0.9150 |
| **[webAI-ColVec1-9b](TODO)** | **0.9413** | **0.6882** | **0.9505** | 0.8758 | 0.9963 | 0.9739 | 0.9839 | 0.9926 | 0.9460 | 0.7956 | 0.9144 |
| [Ops-Colqwen3-4B](https://huggingface.co/OpenSearch-AI/Ops-Colqwen3-4B) | 0.9180 | 0.6650 | 0.9400 | 0.9080 | 0.9960 | 0.9730 | 0.9800 | 0.9960 | 0.9360 | 0.8240 | 0.9140 |
| **[SauerkrautLM-ColQwen3-8b-v0.1](https://huggingface.co/VAGOsolutions/SauerkrautLM-ColQwen3-8b-v0.1)** | 0.9380 | 0.6470 | 0.9450 | 0.9040 | 0.9860 | 0.9650 | 0.9680 | 0.9930 | 0.9220 | 0.8400 | 0.9110 |
| **[webAI-ColVec1-4b](TODO)** | 0.9258 | 0.6773 | 0.9412 | 0.8764 | **1.0000** | 0.9703 | 0.9721 | **1.0000** | 0.9414 | 0.7950 | 0.9100 |
---
## 💻 Usage
The processor exposes three primary methods for encoding inputs and computing retrieval scores.
#### `process_images(images, max_length=None)`
Encodes a batch of document images into model-ready tensors. Pass the result directly to the model with `**batch`.
| Parameter | Type | Description |
| ------------ | ----------------------- | ------------------------------------------------------------------- |
| `images` | `List[PIL.Image.Image]` | Document page images. Each image is automatically converted to RGB. |
| `max_length` | `int` | `None` |
```python
batch = processor.process_images(images=pil_images)
batch = {k: v.to(device) for k, v in batch.items()}
embeddings = model(**batch) # shape: (B, seq_len, embed_dim)
```
---
#### `process_queries(texts, max_length=None)`
Encodes a batch of text queries into model-ready tensors.
| Parameter | Type | Description |
| ------------ | ----------- | ------------------------------- |
| `texts` | `List[str]` | Natural-language query strings. |
| `max_length` | `int` | `None` |
```python
batch = processor.process_queries(texts=["What is the revenue for Q3?"])
batch = {k: v.to(device) for k, v in batch.items()}
embeddings = model(**batch) # shape: (B, seq_len, embed_dim)
```
---
#### `score_multi_vector(qs, ps, batch_size=128, device=None)`
Computes ColBERT-style **MaxSim** late-interaction scores between a list of query embeddings and a list of passage (document) embeddings. For each query token, the maximum dot product across all passage tokens is found; these maxima are summed to produce a single scalar score per (query, passage) pair.
| Parameter | Type | Description |
| ------------ | -------------------------- | ---------------------------------------------------------------------- |
| `qs` | `List[Tensor]` or `Tensor` | Query embeddings. Each tensor has shape `(seq_len_q, embed_dim)`. |
| `ps` | `List[Tensor]` or `Tensor` | Passage embeddings. Each tensor has shape `(seq_len_p, embed_dim)`. |
| `batch_size` | `int` | Number of queries processed per inner loop iteration (default: `128`). |
| `device` | `str` | `torch.device` |
Returns a `torch.Tensor` of shape `(n_queries, n_passages)` on CPU in `float32`. Higher scores indicate greater relevance.
```python
scores = processor.score_multi_vector(query_embeddings, doc_embeddings)
# scores[i, j] is the relevance of document j to query i
best_doc_per_query = scores.argmax(dim=1)
```
### Prerequisites
We strongly suggest `flash-attn` to be installed. If not, please change to `attention_impl="sdpa"`
Currently we only support `torch==2.8.0`, for higher pytorch version, please build flash attention manually, otherwise performance throughput could be low. Also, Note that `torch==2.8.0` supports Python Versions: `>= 3.9` and `<= 3.13`.
```bash
pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128
pip install transformers pillow requests
pip install flash-attn --no-build-isolation
```
### Inference Code
```python
import torch
from transformers import AutoModel, AutoProcessor
from PIL import Image, UnidentifiedImageError
import requests
from io import BytesIO
# Configuration
MODEL_ID = "webAI-Official/webAI-ColVec1-4b"
DTYPE = torch.bfloat16
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Load Model & Processor
processor = AutoProcessor.from_pretrained(
MODEL_ID,
trust_remote_code=True,
)
model = AutoModel.from_pretrained(
MODEL_ID,
dtype=DTYPE,
attn_implementation="flash_attention_2",
trust_remote_code=True,
device_map=DEVICE,
).eval()
# Sample Data
queries = [
"Retrieve the city of Singapore",
"Retrieve the city of Beijing"
]
docs = [
"https://upload.wikimedia.org/wikipedia/commons/2/27/Singapore_skyline_2022.jpg",
"https://upload.wikimedia.org/wikipedia/commons/6/61/Beijing_skyline_at_night.JPG"
]
def load_image(url: str) -> Image.Image:
# Some CDNs (e.g., Wikimedia) expect a browser-like UA to avoid 403s.
for headers in ({}, {"User-Agent": "Mozilla/5.0 (compatible; ColQwen3-demo/1.0)"}):
resp = requests.get(url, headers=headers, timeout=10)
if resp.status_code == 403:
continue
resp.raise_for_status()
try:
return Image.open(BytesIO(resp.content)).convert("RGB")
except UnidentifiedImageError as e:
raise RuntimeError(f"Failed to decode image from {url}") from e
raise RuntimeError(f"Could not fetch image (HTTP 403) from {url}; try downloading locally and loading from file path.")
# Helper Functions
def encode_queries(texts, batch_size=8):
outputs = []
for start in range(0, len(texts), batch_size):
batch = processor.process_queries(texts=texts[start : start + batch_size])
batch = {k: v.to(DEVICE) for k, v in batch.items()}
with torch.inference_mode():
embeddings = model(**batch)
vecs = embeddings.to(torch.bfloat16).cpu()
outputs.extend(vecs)
return outputs
def encode_docs(urls, batch_size=4):
pil_images = [load_image(url) for url in urls]
outputs = []
for start in range(0, len(pil_images), batch_size):
batch_imgs = pil_images[start : start + batch_size]
features = processor.process_images(images=batch_imgs)
features = {k: v.to(DEVICE) if isinstance(v, torch.Tensor) else v for k, v in features.items()}
with torch.inference_mode():
embeddings = model(**features)
vecs = embeddings.to(torch.bfloat16).cpu()
outputs.extend(vecs)
return outputs
# Execution
query_embeddings = encode_queries(queries)
doc_embeddings = encode_docs(docs)
# MaxSim Scoring
scores = processor.score_multi_vector(query_embeddings, doc_embeddings)
print(scores)
```
---
## ⚖️ Strengths & Limitations
### Strengths
- **Performance:** State of the art retrieval performance on ViDoRe V1 & V3 dataset with excellent performance on multimodal document retrieval.
- **Complex Layouts:** Excellent handling of chart-rich PDFs, domain-specific documents.
- **End-to-end Retrieval:** Capable of OCR-free retrieval on unseen multimodal documents without using an intermediate vision LLM to generate summary for retrieval.
- **Multilingualism:** Strong performance on non-English document inputs.
### Limitations
- **Storage Cost:** Still larger than single‑vector baselines despite the smaller token dimension.
### License & Data
[LICENSE](https://huggingface.co/webAI-Official/webAI-ColVec1-4b/blob/main/LICENSE.md)
## 📚 Citation
If you use this model, please cite:
```bibtex
@misc{webAI-ColVec1,
title={webAI-ColVec1: Late-Interaction Multi-Vector Embedding Model for Visual Document Retrieval},
author={webAI},
year={2026},
url={https://huggingface.co/webAI-Official/webAI-ColVec1-4b}
}
``` |