Update readme

README.md

@@ -59,7 +59,54 @@ model-index:
     - name: Dev CER (+LM)
       type: cer
       value: 11.01
+base_model:
+- jonatasgrosman/wav2vec2-large-xlsr-53-english
 ---
+# Disclaimer and Requirements
+
+This model is a clone of [**jonatasgrosman/wav2vec2-large-xlsr-53-english**](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) compressed using ZipNN. Compressed losslessly to 88% its original size, ZipNN saved ~0.2GB in storage and potentially ~4PB in data transfer **monthly**.
+
+### Requirement
+
+In order to use the model, ZipNN is necessary:
+```bash
+pip install zipnn
+```
+### Use This Model
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+from zipnn import zipnn_hf
+
+zipnn_hf()
+
+
+pipe = pipeline("automatic-speech-recognition", model="royleibov/wav2vec2-large-xlsr-53-english")
+```
+```python
+# Load model directly
+from transformers import AutoProcessor, AutoModelForCTC
+from zipnn import zipnn_hf
+
+zipnn_hf()
+
+processor = AutoProcessor.from_pretrained("royleibov/wav2vec2-large-xlsr-53-english")
+model = AutoModelForCTC.from_pretrained("royleibov/wav2vec2-large-xlsr-53-english")
+```
+### ZipNN
+ZipNN also allows you to seemlessly save local disk space in your cache after the model is downloaded.
+
+To compress the cached model, simply run:
+```bash
+python zipnn_compress_path.py safetensors --model royleibov/granite-3.0-8b-instruct-ZipNN-Compressed --hf_cache
+```
+
+The model will be decompressed automatically and safely as long as `zipnn_hf()` is added at the top of the file like in the [example above](#use-this-model).
+
+To decompress manualy, simply run:
+```bash
+python zipnn_decompress_path.py --model royleibov/granite-3.0-8b-instruct-ZipNN-Compressed --hf_cache
+```
 
 # Fine-tuned XLSR-53 large model for speech recognition in English