Update app.py

app.py

@@ -1,28 +1,29 @@
 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)
-
-###############
-# Load datasets
-###############
-raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
-logger.info(
-    f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
-)
-
-################
-# Load tokenizer
-################
-tokenizer = get_tokenizer(model_args, data_args)
-
-#####################
-# Apply chat template
-#####################
-raw_datasets = raw_datasets.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer, "task": "sft"})
-train_dataset = raw_datasets["train"]
-eval_dataset = raw_datasets["test"]
+def template(base_model, trained_adapter, token):
+    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)
+    
+    ###############
+    # Load datasets
+    ###############
+    raw_datasets = get_datasets(data_args, splits=data_args.dataset_splits)
+    logger.info(
+        f"Training on the following datasets and their proportions: {[split + ' : ' + str(dset.num_rows) for split, dset in raw_datasets.items()]}"
+    )
+    
+    ################
+    # Load tokenizer
+    ################
+    tokenizer = get_tokenizer(model_args, data_args)
+    
+    #####################
+    # Apply chat template
+    #####################
+    raw_datasets = raw_datasets.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer, "task": "sft"})
+    train_dataset = raw_datasets["train"]
+    eval_dataset = raw_datasets["test"]
 
 with gr.Blocks() as demo:
     gr.Markdown("## AutoTrain Merge Adapter")