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@@ -12,7 +12,7 @@
6. Handling the Risks of Language Models ([slides](https://github.com/NathanGodey/AdvancedNLP/raw/main/slides/pdf/course6_risks.pdf) / lab session)
7. Advanced NLP tasks ([slides](https://github.com/NathanGodey/AdvancedNLP/raw/main/slides/pdf/course7advanced.pdf) / lab session)
8. Domain-specific NLP ([slides](https://github.com/NathanGodey/AdvancedNLP/raw/main/slides/pdf/course8_specific.pdf) / lab session)
-9. Multilingual NLP ([slides](https://github.com/NathanGodey/AdvancedNLP/raw/main/slides/Course%205%20-%20Multilingual%20NLP.pdf) / lab session)
+9. Multilingual NLP ([slides](https://github.com/NathanGodey/AdvancedNLP/blob/main/slides/pdf/Course%209%20-%20Multilingual%20NLP.pdf) / [lab session](https://colab.research.google.com/drive/11TX-q-hAdFiSeMVqFp1VCXhi_Ifoj8Rp?usp=sharing))
10. Multimodal NLP ([slides](https://docs.google.com/presentation/d/1K2DgnPSOGXB1hQ4FZoUU-5ppJ4dn_sLC41Ecwmxi2Zk/edit?usp=sharing) / lab session)
## Evaluation
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diff --git a/markdown/course3_lm.md b/markdown/course3_lm.md
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---
### Decoders - Inference speed
* For greedy decoding without prefix:
- * $n$ passes with sequences of length $n$
+ * $n$ passes with sequences of length $1\leq t \leq n$
* Each pass is $O(n^2)$
* Complexity: $O(n^3)$
* Other decoding are more costly
diff --git a/markdown/course4_efficiency.md b/markdown/course4_efficiency.md
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* `float16`: reduces memory usage, good with V100-gen GPUs
* `bfloat16`: more stability, but only usable with A100-gen GPUs
+---
+### Training LMs - (b)float16
+
+