Update README.md

README.md

@@ -30,8 +30,7 @@ You can utilize AlpaGo to perform natural language processing tasks. Here's an e
 ```python
 from peft import PeftModel
 import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GenerationConfig
 model_id = "EleutherAI/gpt-neox-20b"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 bnb_config = BitsAndBytesConfig(
@@ -42,4 +41,35 @@ bnb_config = BitsAndBytesConfig(
 )
 model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0})
 model = PeftModel.from_pretrained(model, "myzens/AlpaGo")
+
+#You can change Here.
+PROMPT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+Write a short story about a lost key that unlocks a mysterious door.
+### Response:"""
+
+inputs = tokenizer(PROMPT, return_tensors="pt")
+input_ids = inputs["input_ids"].cuda()
+
+generation_config = GenerationConfig(
+    temperature=0.6,
+    top_p=0.95,
+    repetition_penalty=1.15,
+
+)
+
+print("Generating...")
+generation_output = model.generate(
+    input_ids=input_ids,
+    generation_config=generation_config,
+    return_dict_in_generate=True,
+    output_scores=True,
+    max_new_tokens=256,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.pad_token_id,
+)
+
+for s in generation_output.sequences:
+    print(tokenizer.decode(s))
+
 ```