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="SimaFarazi//private/var/folders/2s/hf9zchm57dg1bs1mbrfj0dbh0000gn/T/tmp39x7dw_m/SimaFarazi/gpt2-ppo")
+generator = pipeline("text-generation", model="SimaFarazi//private/var/folders/2s/hf9zchm57dg1bs1mbrfj0dbh0000gn/T/tmpoglpu1m8/SimaFarazi/gpt2-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("SimaFarazi//private/var/folders/2s/hf9zchm57dg1bs1mbrfj0dbh0000gn/T/tmp39x7dw_m/SimaFarazi/gpt2-ppo")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("SimaFarazi//private/var/folders/2s/hf9zchm57dg1bs1mbrfj0dbh0000gn/T/tmp39x7dw_m/SimaFarazi/gpt2-ppo")
+tokenizer = AutoTokenizer.from_pretrained("SimaFarazi//private/var/folders/2s/hf9zchm57dg1bs1mbrfj0dbh0000gn/T/tmpoglpu1m8/SimaFarazi/gpt2-ppo")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("SimaFarazi//private/var/folders/2s/hf9zchm57dg1bs1mbrfj0dbh0000gn/T/tmpoglpu1m8/SimaFarazi/gpt2-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 sha256:0833d10384957d158ed028124ae9ce29e56d50c7f3641676f9371929b5598d7b
+oid sha256:89c26c7bc444304d8181fe4580c9e6d4304a6519e6f128fa8505997a88aeb012
 size 497777468