tokenizer and torch variables fixed

README.md

@@ -41,33 +41,34 @@ base_model:
 ### Direct Use
 
 ```python
-  from transformers import AutoTokenizer, pipeline
-
-  model_name = "NCSOFT/Llama-3-OffsetBias-RM-8B"
-  rm_tokenizer = AutoTokenizer.from_pretrained(model_name)
-  rm_pipe = pipeline(
-      "sentiment-analysis",
-      model=model_name,
-      device="auto",
-      tokenizer=rm_tokenizer,
-      model_kwargs={"torch_dtype": torch.bfloat16}
-  )
-
-  pipe_kwargs = {
-      "return_all_scores": True,
-      "function_to_apply": "none",
-      "batch_size": 1
-  }
-
-  chat = [
-   {"role": "user", "content": "Hello, how are you?"},
-   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
-   {"role": "user", "content": "I'd like to show off how chat templating works!"},
-  ]
-
-  test_texts = [tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False).replace(tokenizer.bos_token, "")]
-  pipe_outputs = rm_pipe(test_texts, **pipe_kwargs)
-  rewards = [output[0]["score"] for output in pipe_outputs]
+from transformers import AutoTokenizer, pipeline
+import torch
+
+model_name = "NCSOFT/Llama-3-OffsetBias-RM-8B"
+rm_tokenizer = AutoTokenizer.from_pretrained(model_name)
+rm_pipe = pipeline(
+    "sentiment-analysis",
+    model=model_name,
+    device="auto",
+    tokenizer=rm_tokenizer,
+    model_kwargs={"torch_dtype": torch.bfloat16}
+)
+
+pipe_kwargs = {
+    "return_all_scores": True,
+    "function_to_apply": "none",
+    "batch_size": 1
+}
+
+chat = [
+ {"role": "user", "content": "Hello, how are you?"},
+ {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
+ {"role": "user", "content": "I'd like to show off how chat templating works!"},
+]
+
+test_texts = [rm_tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False).replace(rm_tokenizer.bos_token, "")]
+pipe_outputs = rm_pipe(test_texts, **pipe_kwargs)
+rewards = [output[0]["score"] for output in pipe_outputs]
 ```
 
 ## Evaluation