End of training

README.md

@@ -1,8 +1,9 @@
 ---
 base_model: llava-hf/llava-1.5-7b-hf
 library_name: peft
-license: llama2
 tags:
+- BDD-X
+- Autonomous-Driving
 - trl
 - sft
 - generated_from_trainer
@@ -16,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # llava-bddx-finetuned
 
-This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on the None dataset.
+This model is a fine-tuned version of [llava-7b-v1.5](https://huggingface.co/llava-7b-v1.5) on the BDD-X-dataset dataset.
 
 ## Model description
 
@@ -37,7 +38,7 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 1.4e-05
 - train_batch_size: 4
-- eval_batch_size: 8
+- eval_batch_size: 4
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear

tokenizer.json

@@ -1,7 +1,14 @@
 {
   "version": "1.0",
   "truncation": null,
-  "padding": null,
+  "padding": {
+    "strategy": "BatchLongest",
+    "direction": "Right",
+    "pad_to_multiple_of": null,
+    "pad_id": 32001,
+    "pad_type_id": 0,
+    "pad_token": "<pad>"
+  },
   "added_tokens": [
     {
       "id": 0,

tokenizer_config.json

@@ -45,12 +45,13 @@
     }
   },
   "bos_token": "<s>",
+  "chat_template": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. {% for message in messages %}{% if message['role'] == 'user' %}USER: {% else %}ASSISTANT: {% endif %}{% for item in message['content'] %}{% if item['type'] == 'text' %}{{ item['text'] }}{% elif item['type'] == 'image' %}<image>{% endif %}{% endfor %}{% if message['role'] == 'user' %} {% else %}{{eos_token}}{% endif %}{% endfor %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "</s>",
   "legacy": false,
   "model_max_length": 1000000000000000019884624838656,
   "pad_token": "<pad>",
-  "padding_side": "left",
+  "padding_side": "right",
   "processor_class": "LlavaProcessor",
   "sp_model_kwargs": {},
   "tokenizer_class": "LlamaTokenizer",