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/tmptpdt1_2u/arianaazarbal/ppo-finetuned-model")
+generator = pipeline("text-generation", model="arianaazarbal//tmp/tmpu6dj7w01/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/tmptpdt1_2u/arianaazarbal/ppo-finetuned-model")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmptpdt1_2u/arianaazarbal/ppo-finetuned-model")
+tokenizer = AutoTokenizer.from_pretrained("arianaazarbal//tmp/tmpu6dj7w01/arianaazarbal/ppo-finetuned-model")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmpu6dj7w01/arianaazarbal/ppo-finetuned-model")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

adapter_config.json

@@ -24,10 +24,10 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "q_proj",
-    "k_proj",
+    "o_proj",
     "v_proj",
-    "o_proj"
+    "q_proj",
+    "k_proj"
   ],
   "task_type": "CAUSAL_LM",
   "trainable_token_indices": null,

adapter_model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:d0deea2490a4e8cf6b88413eda4ef11940cc91705fb6d75968dbe880f41979bc
 size 40400200

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:8972728555d3821281ff050a93bdca6d904c1cfb33b498f0f8e9138a42492af2
+oid sha256:1db72a3ab3141e17bd54bfb9fb31d38b29129500c4b1b57f42fdb659267dff1e
 size 16277