Mr-FineTuner/Phi-3-medium-4k-instruct_2Epoch

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model_name =   "unsloth/Phi-3-medium-4k-instruct",

model = FastLanguageModel.get_peft_model( model, r = 32, # Reduced from 64 for better generalization target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], lora_alpha = 16, # Reduced from 32 (alpha = r/2 is common) lora_dropout = 0.1, # Slight regularization bias = "none", use_gradient_checkpointing = "unsloth", random_state = 3407, )

Map: 100%  2920/2920 [00:01<00:00, 1700.75 examples/s] [730/730 25:49] Test Loss: 1.0054

Step Training Loss Validation Loss 500 0.958900 0.952982 1000 0.829200 0.964493 1500 0.720900 1.007655 2000 0.467300 1.133889 2500 0.412300 1.183991 3000 0.389500 1.219755 3500 0.385400 1.237054

trainer = SFTTrainer( model=model, tokenizer=tokenizer, train_dataset=train_dataset_transformed, eval_dataset=val_dataset_transformed, max_seq_length=max_seq_length, dataset_num_proc=2, packing=False, args=TrainingArguments( per_device_train_batch_size=4, # Increased batch size gradient_accumulation_steps=2, # Reduced from 4 warmup_ratio=0.05, # Better than fixed 5 steps for 20K samples num_train_epochs=2, # Compromise between 1 and 3 learning_rate=1e-4, # Reduced from 2e-4 fp16=not is_bfloat16_supported(), bf16=is_bfloat16_supported(), logging_steps=50, optim="adamw_8bit", weight_decay=0.02, # Increased regularization lr_scheduler_type="cosine", # Better than linear seed=3407, output_dir="outputs", evaluation_strategy="steps", eval_steps=500, # More frequent validation save_strategy="steps", save_steps=500, load_best_model_at_end=True, metric_for_best_model="eval_loss", # Changed from "loss" greater_is_better=False, ), )

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