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Model: ankitkushwaha90/tech3space3-0.6B
Source: Original Platform

full_fine_tunning_model_SFTTraining..py

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+# train_fft.py
+
+import os
+import torch
+
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from trl import SFTTrainer, SFTConfig
+
+# ============================================================
+# PATHS
+# ============================================================
+
+MODEL_PATH = "./Qwen3-0.6B"
+DATASET_PATH = "./dataset/train.jsonl"
+OUTPUT_DIR = "./outputs/qwen3_0.6b_fft"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# ============================================================
+# DATASET
+# ============================================================
+
+print("Loading dataset...")
+dataset = load_dataset(
+    "json",
+    data_files=DATASET_PATH,
+    split="train"
+)
+print(f"Dataset size: {len(dataset)}")
+
+# ============================================================
+# TOKENIZER
+# ============================================================
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(
+    MODEL_PATH,
+    trust_remote_code=True
+)
+
+# Fix for models that don't have pad_token
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# ============================================================
+# MODEL
+# ============================================================
+
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_PATH,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"          # Optional: helps with memory on single GPU
+)
+
+model.config.use_cache = False
+
+# ============================================================
+# TRAINING CONFIG
+# ============================================================
+
+training_args = SFTConfig(
+    output_dir=OUTPUT_DIR,
+    num_train_epochs=3,
+    learning_rate=5e-6,
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=16,
+    bf16=True,
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=200,
+    save_total_limit=2,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.03,
+    max_length=512,
+    packing=False,
+    gradient_checkpointing=True,
+    report_to="none",
+    # Optional but recommended:
+    dataloader_num_workers=2,
+    remove_unused_columns=False,
+)
+
+# ============================================================
+# TRAINER
+# ============================================================
+
+trainer = SFTTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+    processing_class=tokenizer,   # Newer TRL uses processing_class
+    # tokenizer=tokenizer,        # You can use this if processing_class doesn't work
+)
+
+# ============================================================
+# TRAIN
+# ============================================================
+
+print("Starting full fine-tuning...")
+trainer.train()
+
+# ============================================================
+# SAVE MODEL
+# ============================================================
+
+print("Saving model...")
+trainer.save_model(OUTPUT_DIR)
+tokenizer.save_pretrained(OUTPUT_DIR)
+
+print("=" * 60)
+print("✅ FULL FINE TUNING COMPLETED")
+print(f"Model saved to: {OUTPUT_DIR}")
+print("=" * 60)