fix

examples/int8_training/fine_tune_blip2_int8.py

@@ -9,7 +9,6 @@ from peft import LoraConfig, get_peft_model
 config = LoraConfig(
     r=16,
     lora_alpha=32,
-    target_modules=["q_proj", "v_proj"],
     lora_dropout=0.05,
     bias="none",
     task_type="VISION_2_SEQ",

src/peft/mapping.py

@@ -46,7 +46,7 @@ TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = {
     "bart": ["q_proj", "v_proj"],
     "gpt2": ["c_attn"],
     "bloom": ["query_key_value"],
-    "blip2": ["q", "v", "q_proj", "v_proj"],
+    "blip-2": ["q", "v", "q_proj", "v_proj"],
     "opt": ["q_proj", "v_proj"],
     "gptj": ["q_proj", "v_proj"],
     "gpt_neox": ["query_key_value"],

src/peft/tuners/lora.py

@@ -415,7 +415,6 @@ class Linear(nn.Linear, LoraLayer):
             return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
 
 
-
 class MergedLinear(nn.Linear, LoraLayer):
     # Lora implemented in a dense layer
     def __init__(