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@ -298,4 +298,3 @@ During the backward pass:
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- **Accuracy:** Provides exact derivatives up to machine precision.
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- **Ease of Use:** Eliminates manual computation of derivatives.
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@ -96,4 +96,3 @@ print(token_ids[:50])
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- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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@ -238,4 +238,3 @@ tensor([[ 367, 2885, 1464, 1807],
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- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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@ -216,4 +216,3 @@ print(input_embeddings.shape) # torch.Size([8, 4, 256])
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- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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@ -429,4 +429,3 @@ For another compact and efficient implementation you could use the [`torch.nn.Mu
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@ -699,4 +699,3 @@ print("Output length:", len(out[0]))
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- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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@ -970,4 +970,3 @@ There 2 quick scripts to load the GPT2 weights locally. For both you can clone t
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@ -62,4 +62,3 @@ def replace_linear_with_lora(model, rank, alpha):
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- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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@ -115,4 +115,3 @@ You can find all the code to fine-tune GPT2 to be a spam classifier in [https://
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- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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@ -105,4 +105,3 @@ You can find an example of the code to perform this fine tuning in [https://gith
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- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
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@ -98,4 +98,3 @@ You should start by reading this post for some basic concepts you should know ab
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{{#endref}}
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