Update app.py

app.py

@@ -9,7 +9,7 @@ import requests as rq
 import gc
 from tokenizers import ByteLevelBPETokenizer
 
-dataset = load_dataset("nroggendorff/openhermes", split="train").select(range(int(1e+5)))
+dataset = load_dataset("nroggendorff/openhermes", split="train").select(range(int(1e+4)))
 
 def get_training_corpus():
     for i in range(0, len(dataset), 1000):
@@ -59,10 +59,10 @@ print(tokenizer.apply_chat_template([{"role": "user", "content": "Why is the sky
 
 config = LlamaConfig(
     vocab_size=tokenizer.vocab_size,
-    hidden_size=int(512 / 8),
-    intermediate_size=int(1024 / 8),
-    num_hidden_layers=int(8 / 4),
-    num_attention_heads=int(8 / 4),
+    hidden_size=int(512 / 1),
+    intermediate_size=int(1024 / 1),
+    num_hidden_layers=int(8 / 1),
+    num_attention_heads=int(8 / 1),
     max_position_embeddings=int(512 / 1),
     rms_norm_eps=1e-6,
     initializer_range=0.02,
@@ -97,7 +97,7 @@ print(dataset['text'][2])
 
 args = TrainingArguments(
     output_dir="mayo",
-    num_train_epochs=1,
+    num_train_epochs=2,
     gradient_accumulation_steps=4,
     per_device_train_batch_size=16,
     learning_rate=1e-5,