Add README.md

README.md

@@ -1,12 +1,9 @@
 ---
 license: llama2
-datasets:
-- allenai/c4
-language:
-- en
-metrics:
-- perplexity
-- accuracy
+datasets: ['allenai/c4']
+language: ['en']
+metrics: ['perplexity', 'accuracy']
+tags: ['acip', 'pytorch']
 base_model:
 - meta-llama/Llama-2-13b-hf
 pipeline_tag: text-generation
@@ -45,10 +42,10 @@ from transformers import AutoModel
 
 model = AutoModel.from_pretrained("MerantixMomentum/acip_llama2_13b", trust_remote_code=True)
 ```
-This will download and create a fully parameterized ACIP model that can be pruned to any compression ratio you wish.
+This will download and create a fully parameterized ACIP model that can be pruned to any compression rate you wish.
 For example,
 ```python
-model.prune_model_by_score(compression_ratio=0.4)
+model.prune_model_by_score(size_ratio=0.4)
 ```
 will prune `model` to 40% if its original size measured in number of parameters, i.e., 60% compression rate.
 A unique feature of ACIP is that this operation is revertible in the sense that you can rerun `model.prune_model_by_score` as often as you like to evaluate your model at different sizes. Finally, you can "commit" to a certain ratio and run
@@ -65,7 +62,7 @@ to save even more memory (we have only tested 4bit quantization with `bitsandbyt
 
 **🚀 That's it! You can now use your compressed model for inference or fine-tuning as any other Causal Language Model from 🤗 transformers.**
 
-**Note**: The parameter `compression_ratio` ranges from 1.0 to 0.0, indicating the model size after compression. For example, 0.4 means that the model has only 40% of the original number of parameters and 1.0 means no compression at all.
+**Note**: The parameter `size_ratio` ranges from 1.0 to 0.0, indicating the model size after compression. For example, 0.4 means that the model has only 40% of the original number of parameters and 1.0 means no compression at all. Alternatively, you can also set `compression_rate` in `prune_model_by_score`, which is equivalent to `size_ratio = 1.0 - compression_rate`.
 
 # Dependencies