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docs/lora_optims.html
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<p>Inspired by <a href="https://github.com/unslothai/unsloth">Unsloth</a>, we’ve implemented two
optimizations for LoRA and QLoRA fine-tuning, supporting both single GPU and multi-GPU
(in the DDP and DeepSpeed settings) training. These include (1) SwiGLU and GEGLU activation function
Triton kernels, and (2) LoRA MLP and attention custom autograd functions. Our goal was
to leverage operator fusion and tensor re-use in order to improve speed and reduce
memory usage during the forward and backward passes of these calculations.</p>
(including the DDP, DeepSpeed, and FSDP2 settings) training. These include (1) SwiGLU
and GEGLU activation function Triton kernels, and (2) LoRA MLP and attention custom
autograd functions. Our goal was to leverage operator fusion and tensor re-use in order
to improve speed and reduce memory usage during the forward and backward passes of
these calculations.</p>
<p>We currently support several common model architectures, including (but not limited to):</p>
<ul>
<li><code>llama</code></li>
@@ -687,7 +688,6 @@ computation path.</p>
<h2 class="anchored" data-anchor-id="future-work">Future Work</h2>
<ul>
<li>Support for additional model architectures</li>
<li>Support for the FSDP setting</li>
<li>Support for dropout and bias</li>
<li>Additional operator fusions</li>
</ul>

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