Update README.md

README.md

@@ -13,18 +13,16 @@ This model only contains the `GaudiConfig` file for running the [Swin Transforme
 **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
-
+- `disable_autocast`: whether to disable autocast; this parameter takes precedence over --bf16 flag and is temporary as some scripts produce nan values. 
+   In those cases this parameter is already present in huggingface topology Habana gaudi_config.json.
+  
 ## 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.
+This model is supported only in mixed precision training with bf16 type.
 
 [Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with Swin with the following command:
 ```bash
@@ -48,7 +46,8 @@ python run_image_classification.py \
     --use_lazy_mode \
     --gaudi_config_name Habana/swin \
     --throughput_warmup_steps 2 \
-    --ignore_mismatched_sizes
+    --ignore_mismatched_sizes \
+    --bf16
 ```
 
 Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.