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    <title>PyPI recent updates for torchblocks-vp</title>
    <link>https://pypi.org/project/torchblocks-vp/</link>
    <description>Recent updates to the Python Package Index for torchblocks-vp</description>
    <language>en</language>    <item>
      <title>3.0.0</title>
      <link>https://pypi.org/project/torchblocks-vp/3.0.0/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Mon, 20 Apr 2026 09:55:21 GMT</pubDate>
    </item>    <item>
      <title>2.2.0</title>
      <link>https://pypi.org/project/torchblocks-vp/2.2.0/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Thu, 02 Apr 2026 03:36:29 GMT</pubDate>
    </item>    <item>
      <title>2.1.5</title>
      <link>https://pypi.org/project/torchblocks-vp/2.1.5/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Mon, 30 Mar 2026 19:19:14 GMT</pubDate>
    </item>    <item>
      <title>2.1.4</title>
      <link>https://pypi.org/project/torchblocks-vp/2.1.4/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Mon, 30 Mar 2026 18:08:58 GMT</pubDate>
    </item>    <item>
      <title>2.1.3</title>
      <link>https://pypi.org/project/torchblocks-vp/2.1.3/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Mon, 30 Mar 2026 16:35:49 GMT</pubDate>
    </item>    <item>
      <title>2.1.2</title>
      <link>https://pypi.org/project/torchblocks-vp/2.1.2/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Mon, 30 Mar 2026 16:25:08 GMT</pubDate>
    </item>    <item>
      <title>2.1.1</title>
      <link>https://pypi.org/project/torchblocks-vp/2.1.1/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Mon, 30 Mar 2026 16:10:28 GMT</pubDate>
    </item>    <item>
      <title>2.1.0</title>
      <link>https://pypi.org/project/torchblocks-vp/2.1.0/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Sun, 29 Mar 2026 16:56:01 GMT</pubDate>
    </item>    <item>
      <title>2.0.2</title>
      <link>https://pypi.org/project/torchblocks-vp/2.0.2/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Sun, 29 Mar 2026 16:53:40 GMT</pubDate>
    </item>    <item>
      <title>2.0.1</title>
      <link>https://pypi.org/project/torchblocks-vp/2.0.1/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Sun, 29 Mar 2026 16:45:19 GMT</pubDate>
    </item>    <item>
      <title>2.0.0</title>
      <link>https://pypi.org/project/torchblocks-vp/2.0.0/</link>
      <description>Typed pluggable Transformer blocks with registries for attention, norm, feedforward, adapters, and position layers.</description>
      <pubDate>Sun, 29 Mar 2026 16:20:11 GMT</pubDate>
    </item>    <item>
      <title>1.1.0</title>
      <link>https://pypi.org/project/torchblocks-vp/1.1.0/</link>
      <description>Pluggable Transformer building blocks: GQA, RoPE, SwiGLU, RMSNorm, Conformer conv, adapters with registry system</description>
      <pubDate>Sat, 28 Mar 2026 18:12:55 GMT</pubDate>
    </item>    <item>
      <title>1.0.0</title>
      <link>https://pypi.org/project/torchblocks-vp/1.0.0/</link>
      <description>Reusable Transformer building blocks: attention, feedforward, normalization, positional encoding</description>
      <pubDate>Sat, 28 Mar 2026 14:31:24 GMT</pubDate>
    </item>  </channel>
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