Upload LLaVA-LoRA model

README.md

@@ -18,6 +18,7 @@ The model was fine-tuned using LoRA with the following configuration:
 ```python
 from peft import PeftModel
 from transformers import AutoProcessor, LlavaForConditionalGeneration
+import torch
 
 # Load base model
 base_model = LlavaForConditionalGeneration.from_pretrained(

adapter_config.json

@@ -1,13 +1,20 @@
 {
   "base_model_name_or_path": "llava-hf/llava-1.5-7b-hf",
+  "peft_type": "LORA",
   "task_type": "CAUSAL_LM",
   "inference_mode": false,
   "r": 8,
   "lora_alpha": 32,
   "lora_dropout": 0.1,
+  "fan_in_fan_out": false,
+  "bias": "none",
   "target_modules": [
     "q_proj",
     "v_proj",
     "k_proj"
-  ]
+  ],
+  "modules_to_save": null,
+  "init_lora_weights": true,
+  "layers_to_transform": null,
+  "layers_pattern": null
 }