Update README.md

README.md

@@ -37,6 +37,8 @@ It was trained on speech data, which makes it particularly adapted to train spee
 
 ## How to Get Started with the Model
 
+### Usage with `transformers`
+
 Use the following code to get started with the Mimi model using a dummy example from the LibriSpeech dataset (~9MB). First, install the required Python packages:
 
 ```
@@ -73,6 +75,10 @@ audio_values = model.decode(encoder_outputs.audio_codes)[0]
 audio_values = model(inputs["input_values"]).audio_values
 ```
 
+### Usage with Moshi
+
+See the main [README](https://github.com/kyutai-labs/moshi) file.
+
 ### Direct Use
 
 Mimi can be used directly as an audio codec for real-time compression and decompression of speech signals. 
@@ -86,9 +92,6 @@ The model is not intended to be used to impersonate other people or any maliciou
 
 The model has been trained with a few safeguards to try to limit potential toxic usages, however our toxicity analysis shows that it behaves in the middle of existing models with respect to textual generation. It has some bias towards certain domains and topics that are over-represented in the training data. Its capabilities are relatively limited so far and it is trained to produce only one voice to avoid impersonation. Yet, we need the perspective in time to establish the sociotechnical limitations. 
 
-## How to Get Started with the Model
-
-See the main [README](https://github.com/kyutai-labs/moshi) file. 
 
 ## Training Details