Update app.py

app.py

@@ -1,4 +1,5 @@
-import DataArguments, ModelArguments, apply_chat_template, get_datasets, get_tokenizer
+import gradio as gr
+from . import DataArguments, ModelArguments, apply_chat_template, get_datasets, get_tokenizer
 
 data_args = DataArguments(chat_template=None, dataset_mixer={'HuggingFaceH4/no_robots': 1.0}, dataset_splits=['train_sft', 'test_sft'], max_train_samples=None, max_eval_samples=None, preprocessing_num_workers=12, truncation_side=None)
 model_args = ModelArguments(base_model_revision=None, model_name_or_path='mistralai/Mistral-7B-v0.1', model_revision='main', model_code_revision=None, torch_dtype='auto', trust_remote_code=True, use_flash_attention_2=True, use_peft=True, lora_r=64, lora_alpha=16, lora_dropout=0.1, lora_target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj'], lora_modules_to_save=None, load_in_8bit=False, load_in_4bit=True, bnb_4bit_quant_type='nf4', use_bnb_nested_quant=False)
@@ -21,4 +22,37 @@ tokenizer = get_tokenizer(model_args, data_args)
 #####################
 raw_datasets = raw_datasets.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer, "task": "sft"})
 train_dataset = raw_datasets["train"]
-eval_dataset = raw_datasets["test"]
+eval_dataset = raw_datasets["test"]
+
+with gr.Blocks() as demo:
+    gr.Markdown("## AutoTrain Merge Adapter")
+    gr.Markdown("Please duplicate this space and attach a GPU in order to use it.")
+    token = gr.Textbox(
+        label="Hugging Face Write Token",
+        value="",
+        lines=1,
+        max_lines=1,
+        interactive=True,
+        type="password",
+    )
+    base_model = gr.Textbox(
+        label="Base Model (e.g. meta-llama/Llama-2-7b-chat-hf)",
+        value="",
+        lines=1,
+        max_lines=1,
+        interactive=True,
+    )
+    trained_adapter = gr.Textbox(
+        label="Trained Adapter Model (e.g. username/autotrain-my-llama)",
+        value="",
+        lines=1,
+        max_lines=1,
+        interactive=True,
+    )
+    submit = gr.Button(value="Merge & Push")
+    op = gr.Markdown(interactive=False)
+    submit.click(merge, inputs=[base_model, trained_adapter, token], outputs=[op])
+
+
+if __name__ == "__main__":
+    demo.launch()