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README.md

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 This collection contains large size versions of cache-aware FastConformer-Hybrid (around 114M parameters) with multiple look-ahead support, trained on a large scale english speech. 
 These models are trained for streaming ASR which be used for streaming applications with a variety of latencies (0ms, 80ms, 480s, 1040ms).
-All models are hybrid with both Transducer and CTC decoders. 
-See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-transducer) for complete architecture details.
 
 
 ## Model Architecture
 
-FastConformer [4] is an optimized version of the Conformer model [1]. The model is trained in a multitask setup with joint Transducer and CTC decoder loss. You may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer) and about Hybrid Transducer-CTC training here: [Hybrid Transducer-CTC](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#hybrid-transducer-ctc). You may find more on how to switch between the Transducer and CTC decoders in the documentations.
+These models are cache-aware versions of Hybrid FastConfomer which are trianed for streaming ASR. You may find more info on cache-aware models here: [Cache-aware Streaming Conformer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#cache-aware-streaming-conformer).
+The models are trained with multiple look-aheads which makes the model to be able to support different latencies. 
+To learn on how to switch between different look-aheads, you may read the documentations on the cache-aware models.
 
-These models are cache-aware versions of Hybrid FastConfomer which are trianed for streaming ASR. You may find more info on cache-aware models here: [Cache-aware Streaming Conformer](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#cache-aware-streaming-conformer). The models are trained with multiple look-aheads which makes the model to be able to support different latencies. To learn on how to switch between different look-aheads, you may read the documentations on the cache-aware models.
+FastConformer [4] is an optimized version of the Conformer model [1], and 
+you may find more information on the details of FastConformer here: [Fast-Conformer Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#fast-conformer). 
+
+The model is trained in a multitask setup with joint Transducer and CTC decoder loss. You can find more about Hybrid Transducer-CTC training here: [Hybrid Transducer-CTC](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#hybrid-transducer-ctc). 
+You may also find more on how to switch between the Transducer and CTC decoders in the documentations.
+
+ 
 
 
 ## Training