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="yuchiz//srv/condor/execute/dir_8989/tmpr34brgk5/yuchiz/lmrl-7b")
+generator = pipeline("text-generation", model="yuchiz//srv/condor/execute/dir_58527/tmpmfyvbgjv/yuchiz/lmrl-7b")
 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("yuchiz//srv/condor/execute/dir_8989/tmpr34brgk5/yuchiz/lmrl-7b")
-model = AutoModelForCausalLMWithValueHead.from_pretrained("yuchiz//srv/condor/execute/dir_8989/tmpr34brgk5/yuchiz/lmrl-7b")
+tokenizer = AutoTokenizer.from_pretrained("yuchiz//srv/condor/execute/dir_58527/tmpmfyvbgjv/yuchiz/lmrl-7b")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("yuchiz//srv/condor/execute/dir_58527/tmpmfyvbgjv/yuchiz/lmrl-7b")
 
 inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
 outputs = model(**inputs, labels=inputs["input_ids"])

adapter_config.json

@@ -19,8 +19,8 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "q_proj",
-    "v_proj"
+    "v_proj",
+    "q_proj"
   ],
   "task_type": "CAUSAL_LM",
   "use_rslora": false