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README.md

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+---
+task_categories:
+  - feature-extraction
+language:
+  - en
+license: apache-2.0
+size_categories:
+  - 10M<n<100M
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train-*.parquet
+      - split: test
+        path: data/test-*.parquet
+      - split: neighbors
+        path: data/neighbors.parquet
+dataset_info:
+  features:
+    - name: id
+      dtype: int64
+    - name: emb
+      sequence: float32
+  splits:
+    - name: train
+      num_examples: 10000000
+    - name: test
+      num_examples: 10000
+    - name: neighbors
+      num_examples: 10000
+tags:
+  - multimodal
+  - embeddings
+  - vector-search
+  - benchmark
+  - image-text
+---
+
+# Multimodal Embedding 10M Benchmark Dataset
+
+A large-scale vector search benchmark dataset containing **10M base vectors** and **10K query vectors** with pre-computed ground truth (top-100 nearest neighbors by Inner Product), generated from multimodal (image + text) inputs.
+
+## Dataset Summary
+
+| Property | Value |
+|----------|-------|
+| Base Vectors | 10,000,000 |
+| Query Vectors | 10,000 |
+| Dimension | 4,096 |
+| Distance Metric | Inner Product (IP) |
+| Top-K Ground Truth | 100 |
+| Vector dtype | float32 |
+| Embedding Model | [Qwen3-VL-Embedding-8B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) |
+| Source Data | [pixparse/cc12m-wds](https://huggingface.co/datasets/pixparse/cc12m-wds) (Conceptual Captions 12M) |
+| Base/Query Overlap | Yes (0.1%, query shards 40-41 included in base) |
+
+## Splits
+
+| Split | Rows | Description |
+|-------|------|-------------|
+| `train` | 10,000,000 | Base vectors (20 parquet shards, 500K rows each) |
+| `test` | 10,000 | Query vectors |
+| `neighbors` | 10,000 | Ground truth: top-100 nearest neighbor IDs per query |
+
+## Schema
+
+**train / test:**
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | int64 | Sequential identifier (0-indexed) |
+| `emb` | list\<float32\> | 4096-dim L2-normalized embedding |
+
+**neighbors:**
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | int64 | Query ID (matches test split) |
+| `neighbors` | list\<int64\> | Top-100 base vector IDs by IP score (descending) |
+
+## Usage
+
+```python
+from datasets import load_dataset
+import numpy as np
+
+ds = load_dataset("WenxingZhu/multimodal-embedding-10M")
+
+# Base and query embeddings
+base_emb = np.array(ds["train"]["emb"])    # (10_000_000, 4096) - streams from 20 shards
+query_emb = np.array(ds["test"]["emb"])    # (10_000, 4096)
+
+# Ground truth neighbors
+neighbors = ds["neighbors"]["neighbors"]   # list of 10K lists, each 100 int64 IDs
+```
+
+### Streaming (recommended for 10M)
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("WenxingZhu/multimodal-embedding-10M", split="train", streaming=True)
+for row in ds:
+    emb = row["emb"]  # list of 4096 floats
+    break
+```
+
+## Generation Details
+
+- **Model**: Qwen3-VL-Embedding-8B via vLLM (pooling runner, bfloat16)
+- **Input**: Image-text pairs from cc12m-wds WebDataset shards, streamed from HuggingFace
+- **Normalization**: L2 normalized post-embedding
+- **Hardware**: 6x NVIDIA A100-80GB (DGX)
+- **Throughput**: ~25 samples/sec per GPU
+- **Total Generation Time**: ~3 days for 10M embeddings
+- **Ground Truth**: Brute-force inner product, chunked numpy computation
+
+## Related
+
+- [multimodal-embedding-1M](https://huggingface.co/datasets/WenxingZhu/multimodal-embedding-1M) — 1M version from cc3m-wds
+
+## License
+
+Apache 2.0