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/tmpby2r0l5t/bachephysicdun/HW2-ppo")
+generator = pipeline("text-generation", model="bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpk164fqbu/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/tmpby2r0l5t/bachephysicdun/HW2-ppo")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpby2r0l5t/bachephysicdun/HW2-ppo")
+tokenizer = AutoTokenizer.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpk164fqbu/bachephysicdun/HW2-ppo")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("bachephysicdun//private/var/folders/t_/g9d82w112xv0f54780gpp8_00000gn/T/tmpk164fqbu/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 sha256:541da5969a13a2849a8147e862f74cab46ec34b2180583071bca95c8bac6eca9
+oid sha256:37efde5eb2e0a05e1227ea9ca253a56a8dab0052ba3fb4cb2361b6f567e6c38c
 size 497777468

tokenizer.json

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