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="dshin//tmp/tmpwsp9x9w9/dshin/flan-t5-ppo-testing-violation")
+generator = pipeline("text-generation", model="dshin//tmp/tmp2rccfo3i/dshin/flan-t5-ppo-testing-violation")
 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("dshin//tmp/tmpwsp9x9w9/dshin/flan-t5-ppo-testing-violation")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmpwsp9x9w9/dshin/flan-t5-ppo-testing-violation")
+tokenizer = AutoTokenizer.from_pretrained("dshin//tmp/tmp2rccfo3i/dshin/flan-t5-ppo-testing-violation")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("dshin//tmp/tmp2rccfo3i/dshin/flan-t5-ppo-testing-violation")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

tokenizer.json

@@ -1,7 +1,21 @@
 {
   "version": "1.0",
-  "truncation": null,
-  "padding": null,
+  "truncation": {
+    "direction": "Right",
+    "max_length": 198,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": {
+      "Fixed": 198
+    },
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 0,
+    "pad_type_id": 0,
+    "pad_token": "<pad>"
+  },
   "added_tokens": [
     {
       "id": 0,