Update README.md

README.md

@@ -32,3 +32,32 @@ The following `bitsandbytes` quantization config was used during training:
 - PEFT 0.5.0
 
 - PEFT 0.5.0
+
+## How to Use
+
+You can load the model and perform inference as follows:
+
+```python
+from transformers import AutoTokenizer , AutoModelForCausalLM
+from peft import PeftConfig  , PeftModel 
+
+ 
+path_or_model_name="llama2-frenchmedmcqa-dpo"
+ 
+config = PeftConfig.from_pretrained(path_or_model_name)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_4bit=True, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+
+model = PeftModel.from_pretrained(model ,path_or_model_name)
+
+
+prompt="### Human: Quelle méthode peut être utilisée pour déterminer la constante d'acidité (7,1 et 10,6) de l'acide valproïque (acide dipropylacétique) ? .\n### Assistant : "
+    
+
+
+input_ids =tokenizer(prompt , return_tensors="pt")
+output= model.generate(**input_ids , max_length=120)
+text_generated =tokenizer.decode(output[0] , skip_special_tokens=True)
+
+print(text_generated)
+```