Comments (3)
rasbt
commented on May 22, 2024
1
Hi there, and thanks for your enthusiasm about this book!
The code is all open source, so no worries about forking it. It's actually nice that you are trying to make it accessible to more readers. Thanks!
I also included a note about your project here in the README.
If you could clarify that this is a fork of this project and link the original GitHub repository at the top of the forked README, that would be very appreciated (just to avoid any potential confusion with readers and the publisher).
Note that I also updated the figures in to link them but not directly include them in the notebooks but link them from my website where I'll keep updating them.
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Intelligence-Manifesto
commented on May 22, 2024
I’m not bot. TT
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Intelligence-Manifesto
commented on May 22, 2024
Thanks！！I will do my best.
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Related Issues (20)
- In 3.3.1, there seems to be a missing image between "The attention weights and context vector calculation are summarized in the figure below:" and "The code below walks through the figure above step by step." HOT 1
- RuntimeError: size mismatch - ch05/03_bonus_pretraining_on_gutenberg HOT 2
- book feedback HOT 1
- Inconsistencies between the code in the book and the notebooks (2.6 Data sampling with a sliding window) HOT 7
- Output of the cell without variable specified (Embedding Layers and Linear Layers) HOT 1
- Wrong number of token ids specified in the notebook (2.7 Creating token embeddings) HOT 1
- Incorrect description of function torch.arange() (2.8 Encoding word positions) HOT 1
- Inconsistencies in output for dropout section (3.5.2 Masking additional attention weights with dropout) HOT 1
- Probably a typo in multi-head attention description (3.6.1 Stacking multiple single-head attention layers) HOT 1
- Solution for Exercise 3.2 is included in the notebook with main code (3.6.1 Stacking multiple single-head attention layers) HOT 1
- Question about implementation of CausalAttention class (3.5.3 Implementing a compact causal self-attention class) HOT 6
- Inconsistencies in unsqueeze operation description in the book and in notebook and its necessity (3.6.2 Implementing multi-head attention with weight splits) HOT 4
- Solution for Exercise 3.3 is included in the notebook with main code (3.6.2 Implementing multi-head attention with weight splits) HOT 1
- Inconsistencies in MHA Wrapper Implementation Between Chapter 3 Main Content and Bonus Material HOT 1
- Chapter 5 - Context Size and the DataLoaders HOT 2
- Feedback: Stripe output from notebook HOT 2
- About endoftext in ch05/03_bonus_pretraining_on_gutenberg/pretraining_simple.py HOT 14
- Contributions for Chinese simplified version HOT 4
- {Q} : Replacing the LlamaDecoderLayer Class hugging Face With New LongNet
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