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generall
2025-01-13 21:41:17 +01:00
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qdrant-landing/content/documentation/advanced-tutorials/pdf-retrieval-at-scale.md
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@@ -225,7 +225,7 @@ We choose to **preserve prefix and postfix multivectors**. Our **pooling** opera
Simplified version of pooling for **ColQwen** / **ColPali** models:
(see the full version in the [tutorial notebook](https://githubtocolab.com/qdrant/examples/blob/master/pdf-retrieval-at-scale/ColPali_ColQwen_Tutorial.ipynb))
(see the full version in the [tutorial notebook](https://githubtocolab.com/qdrant/examples/blob/master/pdf-retrieval-at-scale/ColPali_ColQwen2_Tutorial.ipynb))
```python
@@ -272,7 +272,7 @@ pooled_by_columns = torch.cat([pooled_by_columns, image_embedding[~mask]])
Upload process is trivial, the only thing to pay attention to is the compute cost for ColPali and ColQwen2 models.
In low-resource environments, it's recommended to use a smaller batch size for embedding and mean pooling.
Full version of the upload code is available in the [tutorial notebook](https://githubtocolab.com/qdrant/examples/blob/master/pdf-retrieval-at-scale/ColPali_ColQwen_Tutorial.ipynb)
Full version of the upload code is available in the [tutorial notebook](https://githubtocolab.com/qdrant/examples/blob/master/pdf-retrieval-at-scale/ColPali_ColQwen2_Tutorial.ipynb)
## Querying PDFs