f

src/AI/AI-llm-architecture/0.-basic-llm-concepts.md

@@ -297,4 +297,3 @@ During the backward pass:
 - **Efficiency:** Avoids redundant calculations by reusing intermediate results.
 - **Accuracy:** Provides exact derivatives up to machine precision.
 - **Ease of Use:** Eliminates manual computation of derivatives.
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src/AI/AI-llm-architecture/1.-tokenizing.md

@@ -96,4 +96,3 @@ print(token_ids[:50])
 
 - [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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src/AI/AI-llm-architecture/2.-data-sampling.md

@@ -238,4 +238,3 @@ tensor([[  367,  2885,  1464,  1807],
 
 - [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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src/AI/AI-llm-architecture/3.-token-embeddings.md

@@ -216,4 +216,3 @@ print(input_embeddings.shape) # torch.Size([8, 4, 256])
 
 - [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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src/AI/AI-llm-architecture/4.-attention-mechanisms.md

@@ -427,4 +427,3 @@ For another compact and efficient implementation you could use the [`torch.nn.Mu
 
 - [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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src/AI/AI-llm-architecture/6.-pre-training-and-loading-models.md

@@ -968,4 +968,3 @@ There 2 quick scripts to load the GPT2 weights locally. For both you can clone t
 
 - [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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src/AI/AI-llm-architecture/README.md

@@ -96,4 +96,3 @@ You should start by reading this post for some basic concepts you should know ab
 {{#ref}}
 7.2.-fine-tuning-to-follow-instructions.md
 {{#endref}}
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