Push model using huggingface_hub.

README.md

@@ -25,7 +25,7 @@ You can then generate text as follows:
 ```python
 from transformers import pipeline
 
-generator = pipeline("text-generation", model="bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmp2v74_bld/bachephysicdun/HW2-ppo")
+generator = pipeline("text-generation", model="bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpby2r0l5t/bachephysicdun/HW2-ppo")
 outputs = generator("Hello, my llama is cute")
 ```
 
@@ -35,8 +35,8 @@ 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("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmp2v74_bld/bachephysicdun/HW2-ppo")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmp2v74_bld/bachephysicdun/HW2-ppo")
+tokenizer = AutoTokenizer.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpby2r0l5t/bachephysicdun/HW2-ppo")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpby2r0l5t/bachephysicdun/HW2-ppo")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:682be1ecfcdea759f5c45b980d2bfd98e8160771e41c88ec3685a83b3f8602d9
+oid sha256:541da5969a13a2849a8147e862f74cab46ec34b2180583071bca95c8bac6eca9
 size 497777468