Update README.md

README.md

@@ -24,18 +24,18 @@ anh-bloomz-7b1-mt-cross-lingual model can be loaded and used via the following c
 ```python
 import re
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained(
-    "laion/anh-bloomz-7b1-mt-cross-lingual",
-)
-tokenizer = AutoTokenizer.from_pretrained(
-    "laion/anh-bloomz-7b1-mt-cross-lingual",
-)
+
+model_name = "laion/anh-bloomz-7b1-mt-cross-lingual"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
 whitespace_tokens_map = {'\n': '<n>', '  ': '<w>'}
-text = "User: Apa yang terjadi pada pertempuran Cannae? Jawab dalam bahasa China.\n"
+text = "User: Apakah kita akan bisa menyembuhkan penyakit kanker? Jawab dalam bahasa China.\n"
 for k, v in whitespace_tokens_map.items():
     text = text.replace(k, v)
 inputs = tokenizer(text, return_tensors="pt")
-tokens = model.generate(**inputs)
+tokens = model.generate(**inputs, max_new_tokens=200, do_sample=True, top_k=40, top_p=0.9, temperature=0.2, 
+                        repetition_penalty=1.2,num_return_sequences=1)
 output = tokenizer.decode(tokens[0], skip_special_tokens=True)
 for v in whitespace_tokens_map.values():
     output = re.sub(rf"{v}\s+(\S+)", rf"{v}\1", output)