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/tmp4eq8fsb5/bachephysicdun/HW2-ppo")
+generator = pipeline("text-generation", model="bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmp2v74_bld/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/tmp4eq8fsb5/bachephysicdun/HW2-ppo")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmp4eq8fsb5/bachephysicdun/HW2-ppo")
+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")
 
 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:beb2ef13cd8c983311eeeecec74bd68cc7b15d0205945af534a82de3f0c6253d
+oid sha256:682be1ecfcdea759f5c45b980d2bfd98e8160771e41c88ec3685a83b3f8602d9
 size 497777468

tokenizer.json

@@ -1,11 +1,6 @@
 {
   "version": "1.0",
-  "truncation": {
-    "direction": "Right",
-    "max_length": 512,
-    "strategy": "LongestFirst",
-    "stride": 0
-  },
+  "truncation": null,
   "padding": null,
   "added_tokens": [
     {