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/tmp4i87spp6/Evan-Lin/Bart-RL-rouge-attractive1-lr5e-06-factor0.1")
+generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpdn6f9_pp/Evan-Lin/Bart-RL-rouge-attractive1-lr5e-06-factor0.1")
 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/tmp4i87spp6/Evan-Lin/Bart-RL-rouge-attractive1-lr5e-06-factor0.1")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp4i87spp6/Evan-Lin/Bart-RL-rouge-attractive1-lr5e-06-factor0.1")
+tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpdn6f9_pp/Evan-Lin/Bart-RL-rouge-attractive1-lr5e-06-factor0.1")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpdn6f9_pp/Evan-Lin/Bart-RL-rouge-attractive1-lr5e-06-factor0.1")
 
 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:e431695c6bccca3efa206a3a2c13383618a6bab45e6d0c1dc880d706c6798ac7
+oid sha256:2dcef3b0f87bc9531878b2b245dce8b5e9ee5c41afd5495d51136a47ed59b235
 size 557974885