SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

[Docs] Fix spelling and grammar mistakes (#28825)

Browse Source 
* Fix typos and grammar mistakes in docs and examples

* Fix typos in docstrings and comments

* Fix spelling of `tokenizer` in model tests

* Remove erroneous spaces in decorators

* Remove extra spaces in Markdown link texts
This commit is contained in:
Klaus Hipp
2024-02-02 08:45:00 +01:00
committed by GitHub
parent 2418c64a1c
commit 721ee783ca
134 changed files with 185 additions and 186 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/source/en/model_doc/mixtral.md
View File

@@ -40,7 +40,7 @@ The original code can be found [here](https://github.com/mistralai/mistral-src).
Mixtral-45B is a decoder-based LM with the following architectural choices:
* Mixtral is a Mixture of Expert (MOE) model with 8 experts per MLP, with a total of 45B paramateres but the compute required is the same as a 14B model. This is because even though each experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dipatched twice (top 2 routing) and thus the compute (the operation required at each foward computation) is just 2 X sequence_length.
* Mixtral is a Mixture of Expert (MOE) model with 8 experts per MLP, with a total of 45B paramateres but the compute required is the same as a 14B model. This is because even though each experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dispatched twice (top 2 routing) and thus the compute (the operation required at each forward computation) is just 2 X sequence_length.
The following implementation details are shared with Mistral AI's first model [mistral](mistral):
* Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens