Push model using huggingface_hub.

README.md

@@ -26,7 +26,7 @@ You can then generate text as follows:
 ```python
 from transformers import pipeline
 
-generator = pipeline("text-generation", model="arianaazarbal//tmp/tmp2ho8e9j8/arianaazarbal/ppo-finetuned-model")
+generator = pipeline("text-generation", model="arianaazarbal//tmp/tmptpdt1_2u/arianaazarbal/ppo-finetuned-model")
 outputs = generator("Hello, my llama is cute")
 ```
 
@@ -36,8 +36,8 @@ 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("arianaazarbal//tmp/tmp2ho8e9j8/arianaazarbal/ppo-finetuned-model")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmp2ho8e9j8/arianaazarbal/ppo-finetuned-model")
+tokenizer = AutoTokenizer.from_pretrained("arianaazarbal//tmp/tmptpdt1_2u/arianaazarbal/ppo-finetuned-model")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmptpdt1_2u/arianaazarbal/ppo-finetuned-model")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

adapter_config.json

@@ -26,8 +26,8 @@
   "target_modules": [
     "q_proj",
     "k_proj",
-    "o_proj",
-    "v_proj"
+    "v_proj",
+    "o_proj"
   ],
   "task_type": "CAUSAL_LM",
   "trainable_token_indices": null,

adapter_model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:f42b35ae1c092012df22d58de5dc83485c1141a224c35aacc9510e363f95d1ec
 size 40400200

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:594f611c59b511e484dff4a89cfcac5c1b9874fee26aab2d656c9b83b5acb94a
+oid sha256:8972728555d3821281ff050a93bdca6d904c1cfb33b498f0f8e9138a42492af2
 size 16277