Update README.md

README.md

@@ -56,7 +56,7 @@ The following code snippet demonstrates how to use Sarvam-Translate using Transf
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "sarvamai/sarvam-translate"  # Replace with any compatible chat model
+model_name = "sarvamai/sarvam-translate"
 
 # Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -82,12 +82,19 @@ text = tokenizer.apply_chat_template(
 model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 
 # Generate the output
-generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.01,
+    num_return_sequences=1
+)
 output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
 output_text = tokenizer.decode(output_ids, skip_special_tokens=True)
 
 print("Input:", input_txt)
 print("Translation:", output_text)
+
 ```
 
 ## VLLM Deployment