Mr-FineTuner/Phi-3-medium-4k-instruct_2Epoch_NewMethod

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Map: 100%  2920/2920 [00:01<00:00, 1602.09 examples/s] [365/365 4:25:54] Test Loss: 1.0123

Step Training Loss Validation Loss 250 0.983800 0.957103 500 0.937900 0.954966 750 0.862300 0.968044 1000 0.800900 0.986456 1250 0.712600 1.017532 1500 0.652100 1.035168 1750 0.600500 1.051357 2000 0.412800 1.152156 2250 0.386200 1.168790 2500 0.377300 1.185837 2750 0.346600 1.223637 3000 0.351300 1.254214 3250 0.321700 1.273642 3500 0.329900 1.280087

train_dataset_transformed = train_dataset_transformed.shuffle(seed=3407)

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=8, # Increased batch size gradient_accumulation_steps=1, # 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=1.5e-4, # Try between 1e-4 and 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_with_restarts", seed=3407, output_dir="outputs", evaluation_strategy="steps", eval_steps=250, # More frequent validation save_strategy="steps", save_steps=250, load_best_model_at_end=True, metric_for_best_model="eval_loss", # Changed from "loss" greater_is_better=False, ), )

another revise

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, )

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