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/tmpfx0c5s07/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
+generator = pipeline("text-generation", model="SummerSigh//tmp/tmp9gtgc8p4/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/tmpfx0c5s07/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("SummerSigh//tmp/tmpfx0c5s07/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
+tokenizer = AutoTokenizer.from_pretrained("SummerSigh//tmp/tmp9gtgc8p4/SummerSigh/T5-Base-Rule-Of-Thumb-RM2")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("SummerSigh//tmp/tmp9gtgc8p4/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": "4.26.1",
+  "transformers_version": "4.22.1",
   "use_cache": true,
   "vocab_size": 32128
 }

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:89f64b157a5d126666c7e12540eb0df6ffb18937b97fd7fb5036602efae51bae
+oid sha256:104ec0bdd2746e68f596096e71cae271ed08b25f5ead4d253d2b13266833582b
 size 990412605