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="XanderJC//tmp/tmpx7vqbd73/XanderJC/llama-3-8b-orca-rlhf")
+generator = pipeline("text-generation", model="XanderJC//tmp/tmptjma5k3d/XanderJC/llama-3-8b-orca-rlhf")
 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("XanderJC//tmp/tmpx7vqbd73/XanderJC/llama-3-8b-orca-rlhf")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("XanderJC//tmp/tmpx7vqbd73/XanderJC/llama-3-8b-orca-rlhf")
+tokenizer = AutoTokenizer.from_pretrained("XanderJC//tmp/tmptjma5k3d/XanderJC/llama-3-8b-orca-rlhf")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("XanderJC//tmp/tmptjma5k3d/XanderJC/llama-3-8b-orca-rlhf")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

adapter_config.json

@@ -20,8 +20,8 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "v_proj",
-    "q_proj"
+    "q_proj",
+    "v_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_dora": false,

adapter_model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:21fd2e5948ee21dc0f71539de1086091bdbbd1d8d0949a18c379146351dd1656
 size 54543184

pytorch_model.bin

@@ -1,3 +1,3 @@
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-oid sha256:ba50ab5155cc2ee6e319e5f4455a7f1a4f5ff3fdb1cc3b44425aa10bf8189f5a
+oid sha256:de91c2883775765d666f20488e13983479351d89fdb514685fe4529916f3760f
 size 17916