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="SummerSigh//tmp/tmpcgg8dhx7/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
+generator = pipeline("text-generation", model="SummerSigh//tmp/tmp2geloj8w/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
 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("SummerSigh//tmp/tmpcgg8dhx7/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("SummerSigh//tmp/tmpcgg8dhx7/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
+tokenizer = AutoTokenizer.from_pretrained("SummerSigh//tmp/tmp2geloj8w/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("SummerSigh//tmp/tmp2geloj8w/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

config.json

@@ -55,7 +55,7 @@
   },
   "tie_word_embeddings": false,
   "torch_dtype": "float32",
-  "transformers_version": "4.27.1",
+  "transformers_version": "4.27.2",
   "use_cache": true,
   "vocab_size": 32128
 }

generation_config.json

@@ -3,5 +3,5 @@
   "decoder_start_token_id": 0,
   "eos_token_id": 1,
   "pad_token_id": 0,
-  "transformers_version": "4.27.1"
+  "transformers_version": "4.27.2"
 }

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b8980435e8ae490bd6f43118c4ce498b8e85b64fc8c93f36bcea9632a6210270
+oid sha256:26686f1189de917b8696c518e80305b90a7fd2306466379a074c37907a2ed2cf
 size 990412605