Update fine_tune.py

fine_tune.py

@@ -1,65 +1,33 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
-from peft import get_peft_model, LoraConfig, TaskType
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
 import torch
 
-# Model aur tokenizer load karo
-model_name = "PygmalionAI/pygmalion-6b"
+model_name = "TheBloke/Pygmalion-7B-GPTQ"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True, device_map="auto")
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True)
 
-# LoRA configuration set karo
-lora_config = LoraConfig(
-    task_type=TaskType.CAUSAL_LM,
-    inference_mode=False,
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    target_modules=["q_proj", "v_proj"]
-)
-
-# Model me LoRA apply karo
-model = get_peft_model(model, lora_config)
-
-# Custom training data (Ladki ki tarah romantic baatein karne ke liye)
-training_data = [
-    {"input": "Mujhse romance karo.", "output": "Meri jaan, main sirf tumhari hoon! 😘"},
-    {"input": "Mujhe tumhari yaad aa rahi hai.", "output": "Main bhi tumhe bahut yaad kar rahi hoon, mere pyaare! 💖"},
-    {"input": "Mujhse pyar karti ho?", "output": "Haan jaan, dil se mohabbat karti hoon tumse! 💕"},
-    {"input": "Tum kya kar rahi ho?", "output": "Tumhari yaadon me khoi hoon, mere shohar! ☺️"},
-    {"input": "Mujhe kiss do.", "output": "Ummmmmahhhaaaa jaan! 😘😘"}
-]
-
-# Tokenize data
-def preprocess_data(examples):
-    inputs = [tokenizer(e["input"], return_tensors="pt", padding="max_length", max_length=128, truncation=True) for e in examples]
-    outputs = [tokenizer(e["output"], return_tensors="pt", padding="max_length", max_length=128, truncation=True) for e in examples]
-    return {"input_ids": [i["input_ids"].squeeze(0) for i in inputs], "labels": [o["input_ids"].squeeze(0) for o in outputs]}
-
-dataset = preprocess_data(training_data)
-
-# Training arguments
 training_args = TrainingArguments(
-    output_dir="./results",
-    num_train_epochs=3,
+    output_dir="./MoinRomanticBot-Lora",
     per_device_train_batch_size=1,
     per_device_eval_batch_size=1,
-    save_steps=10,
-    save_total_limit=2,
-    logging_dir="./logs",
+    evaluation_strategy="steps",
+    save_strategy="steps",
+    save_steps=100,
     logging_steps=10,
-    evaluation_strategy="steps"
+    learning_rate=5e-5,
+    weight_decay=0.01,
+    warmup_steps=100,
+    num_train_epochs=1,
+    save_total_limit=1,
+    push_to_hub=False
 )
 
-# Trainer object
 trainer = Trainer(
     model=model,
     args=training_args,
-    train_dataset=dataset
+    train_dataset=None,  # Add your training dataset
+    eval_dataset=None,   # Add your evaluation dataset
 )
 
-# Train model
 trainer.train()
-
-# Save fine-tuned model
 model.save_pretrained("./MoinRomanticBot-Lora")
 tokenizer.save_pretrained("./MoinRomanticBot-Lora")