Merge pull request #165 from mistralai/doc/v0.0.105

Update docs to v0.0.105

docs/capabilities/guardrailing.mdx

@@ -182,6 +182,10 @@ The table below describes the types of content that can be detected in the moder
 | PII | Content that requests, shares, or attempts to elicit personal identifying information such as full names, addresses, phone numbers, social security numbers, or financial account details. |
 
 
+### Cookbook
+Our [moderation cookbook](https://colab.research.google.com/github.com/mistralai/cookbook/tree/main/mistral/moderation/system-level-guardrails.ipynb) provides a concrete example of how to use the Moderation service to implement system level guardrails.  
+For a more broad view, there is also a more [explorative cookbook](https://colab.research.google.com/github.com/mistralai/cookbook/tree/main/mistral/moderation/moderation-explored.ipynb).
+
 ### FAQ
 Q: What is the distribution of false-positive and false-negative results on the new moderation API models. Specifically, will they be more likely to flag something as harmful when it is not or not flag something that is harmful?
 

docs/guides/finetuning_sections/_04_faq.md

@@ -47,7 +47,7 @@ For Mistral API, you can use the `auto_start=False` argument as mentioned in the
 
 ### What is the recommended learning rate? 
 
-For LoRA fine-tuning, we recommended 1e-4 (default) or 1e-5. 
+For LoRA fine-tuning, we recommend 1e-4 (default) or 1e-5. 
 
 Note that the learning rate we define is the peak learning rate, instead of a flat learning rate. The learning rate follows a linear warmup and cosine decay schedule. During the warmup phase, the learning rate is linearly increased from a small initial value to a larger value over a certain number of training steps. After the warmup phase, the learning rate is decayed using a cosine function.
 
@@ -99,4 +99,4 @@ for file in output_file_objects:
 
 # now you should see three jsonl files under 500MB
 ```
-</details>
+</details>