Update README.md

README.md

@@ -35,8 +35,45 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+```python
 
+
+%%time
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
+
+from peft import PeftModel
+model_name='google/gemma-2b'
+inference_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    low_cpu_mem_usage=True,
+    # use_flash_attention_2=True,
+   
+)
+inference_model.resize_token_embeddings(len(tokenizer))
+
+inference_model = PeftModel.from_pretrained(inference_model, "hemanthkandimalla/HemanthLLM_tiny1.1b")
+
+inference_model.eval()
+text="attacks names"
+tokenizer_input=tokenizer(text=text, return_tensors="pt")
+output_tokens = inference_model.generate(
+    **tokenizer_input,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.2,
+    top_p=0.95,
+    top_k=50,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id,
+)
+text=tokenizer.decode(output_tokens[0], skip_special_tokens=False)
+
+print(text)
+
+```
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->