Push model using huggingface_hub.

README.md

@@ -24,7 +24,7 @@ You can then generate text as follows:
 ```python
 from transformers import pipeline
 
-generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp_9jcwfcy/Evan-Lin/Bart-abs-Amazon-attractive1")
+generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpmdlumop1/Evan-Lin/Bart-abs-Amazon-attractive1")
 outputs = generator("Hello, my llama is cute")
 ```
 
@@ -34,8 +34,8 @@ If you want to use the model for training or to obtain the outputs from the valu
 from transformers import AutoTokenizer
 from trl import AutoModelForCausalLMWithValueHead
 
-tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp_9jcwfcy/Evan-Lin/Bart-abs-Amazon-attractive1")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp_9jcwfcy/Evan-Lin/Bart-abs-Amazon-attractive1")
+tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpmdlumop1/Evan-Lin/Bart-abs-Amazon-attractive1")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpmdlumop1/Evan-Lin/Bart-abs-Amazon-attractive1")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:45159959e25be4d0c6132760535210e8f9fb633363bf708cc1d75e632cbd6259
+oid sha256:961d7931a14e89321c3f18ae1a29af1e88ef0ba7ce7947ca846301a90e66f519
 size 557974885