Update README.md

README.md

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 license: apache-2.0
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+
+[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Transformers library and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading and fine-tuning on single- and multi-HPU settings for different downstream tasks.
+Learn more about how to take advantage of the power of Habana HPUs to train Transformers models at [hf.co/Habana](https://huggingface.co/Habana).
+
+## Bert Base model HPU configuration
+
+This model contains just the `GaudiConfig` file for running the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model on Habana's Gaudi processors (HPU).
+
+**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_User_Guide/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
+
+## Usage
+
+The model is instantiated the same way as in the Transformers library.
+The only difference is that the Gaudi configuration has to be loaded and provided to the trainer:
+
+```
+from optimum.habana import GaudiConfig, GaudiTrainer, GaudiTrainingArguments
+from transformers import BertTokenizer, BertModel
+
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+model = BertModel.from_pretrained("bert-base-uncased")
+gaudi_config = GaudiConfig.from_pretrained("Habana/bert-base-uncased")
+args = GaudiTrainingArguments(
+    output_dir="/tmp/output_dir",
+    use_habana=True,
+    use_lazy_mode=True,
+)
+
+trainer = GaudiTrainer(
+    model=model,
+    gaudi_config=gaudi_config,
+    args=args,
+    tokenizer=tokenizer,
+)
+trainer.train()
+```