Update README.md

README.md

@@ -76,6 +76,28 @@ It results in balanced probabilities of gendered tokens in the model's output, a
 The method for obtaining `P_c` is based on the Partial Least Square algorithm.
 For more details, please refer to the [paper](https://openreview.net/pdf?id=XIZEFyVGC9).
 
+## Use
+
+Following snippet shows the basic usage od DAMA for text generation.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+DAMA_SIZE= '7B'
+OUTPUT_DIR = 'output'
+model = AutoModelForCausalLM.from_pretrained(f"ufal/DAMA-{DAMA_SIZE}", offload_folder=OUTPUT_DIR,
+                                            torch_dtype=torch.float16, low_cpu_mem_usage=True, 
+                                            device_map='auto')
+
+tokenizer = AutoTokenizer.from_pretrained(f"ufal/DAMA-{DAMA_SIZE}", use_fast=True, return_token_type_ids=False)
+
+prompt = "The lifeguard laughed because"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+generate_ids = model.generate(inputs.input_ids, max_length=30)
+tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0]
+```
+
 ## Evaluation
 
 We evaluate the models on multiple benchmarks to assess gender bias and language understanding capabilities.