Update README.md

README.md

@@ -1,3 +1,62 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
+It provides a set of tools enabling easy and fast model loading, training and inference 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 and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana).
+
+## Llama model HPU configuration
+
+This model only contains the `GaudiConfig` file for running [Falcon models](https://huggingface.co/tiiuae) on Habana's Gaudi processors (HPU).
+
+**This model contains no model weights, only a GaudiConfig.**
+
+This enables to specify:
+- `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 PyTorch's autocast 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.
+
+[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/language-modeling/run_clm.py) is a causal language modeling example script to pre-train/fine-tune a model. You can run it with Falcon with the following command:
+```bash
+LOWER_LIST=ops_bf16.txt python3 run_lora_clm.py \
+    --model_name_or_path tiiuae/falcon-40b \
+    --dataset_name timdettmers/openassistant-guanaco \
+    --bf16 True \
+    --output_dir ./model_lora_falcon \
+    --num_train_epochs 3 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 16 \
+    --evaluation_strategy "no" \
+    --save_strategy "no" \
+    --learning_rate 3e-4 \
+    --max_grad_norm  0.3 \
+    --warmup_ratio  0.03 \
+    --lr_scheduler_type "constant" \
+    --logging_steps 1 \
+    --do_train \
+    --use_habana \
+    --use_lazy_mode \
+    --pipelining_fwd_bwd \
+    --throughput_warmup_steps 3 \
+    --lora_rank=64 \
+    --lora_alpha=16 \
+    --lora_dropout=0.1 \
+    --lora_target_modules "query_key_value" "dense" "dense_h_to_4h" "dense_4h_to_h" \
+    --dataset_concatenation \
+    --max_seq_length 256 \
+    --low_cpu_mem_usage True \
+    --adam_epsilon 1e-08 \
+    --do_eval \
+    --validation_split_percentage 5
+```
+
+You will need to install the [PEFT](https://huggingface.co/docs/peft/index) library with `pip install peft` to run the command above.
+
+Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.