a

src/AI/AI-Deep-Learning.md

@@ -435,3 +435,4 @@ Moreover, to generate an image from a text prompt, diffusion models typically fo
 
 
 {{#include ../banners/hacktricks-training.md}}
+

src/AI/AI-Model-Data-Preparation-and-Evaluation.md

@@ -240,3 +240,4 @@ The confusion matrix can be used to calculate various evaluation metrics, such a
 
 
 {{#include ../banners/hacktricks-training.md}}
+

src/AI/AI-Reinforcement-Learning-Algorithms.md

@@ -77,3 +77,4 @@ SARSA is an **on-policy** learning algorithm, meaning it updates the Q-values ba
 On-policy methods like SARSA can be more stable in certain environments, as they learn from the actions actually taken. However, they may converge more slowly compared to off-policy methods like Q-Learning, which can learn from a wider range of experiences.
 
 {{#include ../banners/hacktricks-training.md}}
+

src/AI/AI-llm-architecture/1.-tokenizing.md

@@ -97,4 +97,3 @@ 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

@@ -239,4 +239,3 @@ 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

@@ -217,4 +217,3 @@ 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

@@ -429,3 +429,4 @@ For another compact and efficient implementation you could use the [`torch.nn.Mu
 
 
 
+

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

@@ -700,4 +700,3 @@ print("Output length:", len(out[0]))
 - [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

@@ -970,3 +970,4 @@ There 2 quick scripts to load the GPT2 weights locally. For both you can clone t
 
 
 
+

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

@@ -63,4 +63,3 @@ def replace_linear_with_lora(model, rank, alpha):
 - [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

@@ -116,4 +116,3 @@ You can find all the code to fine-tune GPT2 to be a spam classifier in [https://
 - [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

@@ -106,4 +106,3 @@ You can find an example of the code to perform this fine tuning in [https://gith
 - [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

@@ -99,3 +99,4 @@ You should start by reading this post for some basic concepts you should know ab
 
 
 
+