Update README.md

README.md

@@ -13,18 +13,15 @@ This model only contains the `GaudiConfig` file for running the [bert-large-unca
 **This model contains no model weights, only a GaudiConfig.**
 
 This enables to specify:
-- `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
-    - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html#configuration-options) for a detailed explanation
-    - `hmp_bf16_ops`: list of operators that should run in bf16
-    - `hmp_fp32_ops`: list of operators that should run in fp32
-    - `hmp_is_verbose`: verbosity
 - `use_fused_adam`: whether to use Habana's custom AdamW implementation
 - `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
+- `use_torch_autocast`: whether to use Torch Autocast for managing mixed precision
 
 ## Usage
 
 The model is instantiated the same way as in the Transformers library.
-The only difference is that there are a few new training arguments specific to HPUs.
+The only difference is that there are a few new training arguments specific to HPUs.\
+It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
 
 [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with BERT Large with the following command:
 ```bash
@@ -43,7 +40,8 @@ python run_qa.py \
   --output_dir /tmp/squad/ \
   --use_habana \
   --use_lazy_mode \
-  --throughput_warmup_steps 2
+  --throughput_warmup_steps 2 \
+  --bf16
 ```
 
 Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.