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README.md

@@ -1,81 +1,10 @@
 ---
-license: apache-2.0
-tags:
-- generated_from_trainer
-datasets:
-- food101
-metrics:
-- accuracy
-model-index:
-- name: vit-base-patch16-224-in21k-finetuned-lora-food101
-  results:
-  - task:
-      name: Image Classification
-      type: image-classification
-    dataset:
-      name: food101
-      type: food101
-      config: default
-      split: train[:5000]
-      args: default
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.96
+library_name: peft
 ---
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# vit-base-patch16-224-in21k-finetuned-lora-food101
-
-This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the food101 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1448
-- Accuracy: 0.96
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
 ## Training procedure
 
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.005
-- train_batch_size: 128
-- eval_batch_size: 128
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 512
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 5
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 9    | 0.5069          | 0.896    |
-| 2.1627        | 2.0   | 18   | 0.1891          | 0.946    |
-| 0.3451        | 3.0   | 27   | 0.1448          | 0.96     |
-| 0.2116        | 4.0   | 36   | 0.1509          | 0.958    |
-| 0.1711        | 5.0   | 45   | 0.1498          | 0.958    |
-
-
 ### Framework versions
 
-- Transformers 4.26.0
-- Pytorch 1.13.1+cu116
-- Datasets 2.9.0
-- Tokenizers 0.13.2
+- PEFT 0.5.0
+
+- PEFT 0.5.0

adapter_config.json

@@ -1,17 +1,23 @@
 {
+  "auto_mapping": {
+    "base_model_class": "ViTForImageClassification",
+    "parent_library": "transformers.models.vit.modeling_vit"
+  },
   "base_model_name_or_path": "google/vit-base-patch16-224-in21k",
   "bias": "none",
-  "enable_lora": null,
   "fan_in_fan_out": false,
   "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
   "lora_alpha": 16,
   "lora_dropout": 0.1,
-  "merge_weights": false,
   "modules_to_save": [
     "classifier"
   ],
   "peft_type": "LORA",
   "r": 16,
+  "revision": null,
   "target_modules": [
     "query",
     "value"

adapter_model.bin

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