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="Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmp_gt_6lgl/Hermi2023/doc2query-ppo-msmarco-43520-121")
+generator = pipeline("text-generation", model="Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmpnkm6s2mj/Hermi2023/doc2query-ppo-msmarco-43520-121")
 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("Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmp_gt_6lgl/Hermi2023/doc2query-ppo-msmarco-43520-121")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmp_gt_6lgl/Hermi2023/doc2query-ppo-msmarco-43520-121")
+tokenizer = AutoTokenizer.from_pretrained("Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmpnkm6s2mj/Hermi2023/doc2query-ppo-msmarco-43520-121")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("Hermi2023//mnt/hdd0/home/buwei/sample/tmp/tmpnkm6s2mj/Hermi2023/doc2query-ppo-msmarco-43520-121")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0cb8b4dbb25ee011f57a516283811ec1619c8d71df5c90f696cea57e327837a1
+oid sha256:baa97947bee85983ae7f18cf21e86ee590d78a7b240601f95c8c764b8db34211
 size 891706649