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.
-

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)
 
-

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)
 
-

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)
 
-

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)
 
-

src/AI/AI-llm-architecture/5.-llm-architecture.md

@@ -697,4 +697,4 @@ print("Output length:", len(out[0]))
 
 ## References
 
-- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
+- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)

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)
 
-

src/AI/AI-llm-architecture/7.0.-lora-improvements-in-fine-tuning.md

@@ -60,4 +60,4 @@ def replace_linear_with_lora(model, rank, alpha):
 
 ## References
 
-- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
+- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)

src/AI/AI-llm-architecture/7.1.-fine-tuning-for-classification.md

@@ -113,4 +113,4 @@ You can find all the code to fine-tune GPT2 to be a spam classifier in [https://
 
 ## References
 
-- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
+- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)

src/AI/AI-llm-architecture/7.2.-fine-tuning-to-follow-instructions.md

@@ -103,4 +103,4 @@ You can find an example of the code to perform this fine tuning in [https://gith
 
 ## References
 
-- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)
+- [https://www.manning.com/books/build-a-large-language-model-from-scratch](https://www.manning.com/books/build-a-large-language-model-from-scratch)

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}}
-