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train.py

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+import json
+import torch
+from pathlib import Path
+from datasets import load_dataset
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    DataCollatorForLanguageModeling,
+    TrainingArguments,
+    Trainer,
+)
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, PeftModel
+
+project = Path("/home/zeus/btl-1")
+base_model_name = "Qwen/Qwen2.5-7B-Instruct"
+max_seq_length = 4096
+train_batch_size = 8
+grad_accum = 2
+epochs = 1
+learning_rate = 2e-4
+
+print("Loading dataset...")
+train_ds = load_dataset("json", data_files=str(project / "data" / "final" / "train.jsonl"), split="train")
+eval_ds = load_dataset("json", data_files=str(project / "data" / "final" / "eval.jsonl"), split="train")
+print(f"train: {len(train_ds)}, eval: {len(eval_ds)}")
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True, use_fast=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+print("Loading model (QLoRA 4-bit)...")
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    trust_remote_code=True,
+    quantization_config=bnb_config,
+    device_map="auto",
+    attn_implementation="sdpa",
+)
+model = prepare_model_for_kbit_training(model)
+model.config.use_cache = False
+model.gradient_checkpointing_enable()
+
+lora_config = LoraConfig(
+    r=64,
+    lora_alpha=128,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+
+print("Tokenizing...")
+def render_messages(messages):
+    return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+
+def to_text(batch):
+    return {"text": [render_messages(m) for m in batch["messages"]]}
+
+train_text = train_ds.map(to_text, batched=True, remove_columns=train_ds.column_names)
+eval_text = eval_ds.select(range(min(500, len(eval_ds)))).map(to_text, batched=True, remove_columns=["messages"])
+
+def tokenize_batch(batch):
+    return tokenizer(batch["text"], truncation=True, max_length=max_seq_length, padding=False)
+
+train_tok = train_text.map(tokenize_batch, batched=True, remove_columns=train_text.column_names)
+eval_tok = eval_text.map(tokenize_batch, batched=True, remove_columns=eval_text.column_names)
+
+collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+training_args = TrainingArguments(
+    output_dir="/home/zeus/btl-1/checkpoints",
+    num_train_epochs=epochs,
+    per_device_train_batch_size=train_batch_size,
+    per_device_eval_batch_size=32,
+    gradient_accumulation_steps=grad_accum,
+    eval_strategy="steps",
+    eval_steps=500,
+    save_strategy="steps",
+    save_steps=500,
+    load_best_model_at_end=True,
+    metric_for_best_model="eval_loss",
+    learning_rate=learning_rate,
+    warmup_ratio=0.03,
+    lr_scheduler_type="cosine",
+    logging_steps=10,
+    save_total_limit=2,
+    bf16=torch.cuda.is_available(),
+    fp16=False,
+    optim="paged_adamw_8bit",
+    report_to="none",
+    gradient_checkpointing=True,
+    remove_unused_columns=False,
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_tok,
+    eval_dataset=eval_tok,
+    data_collator=collator,
+)
+
+print("Starting training...")
+train_result = trainer.train()
+trainer.save_state()
+print(f"Training complete: {train_result}")
+
+print("Saving adapter...")
+adapter_dir = project / "artifacts" / "qlora-adapter"
+adapter_dir.mkdir(parents=True, exist_ok=True)
+model.save_pretrained(adapter_dir)
+tokenizer.save_pretrained(adapter_dir)
+print(f"Adapter saved to {adapter_dir}")
+
+print("Loading best checkpoint...")
+best = trainer.state.best_model_checkpoint
+if best:
+    print(f"Best checkpoint: {best}")
+    reloaded_base = AutoModelForCausalLM.from_pretrained(
+        base_model_name,
+        trust_remote_code=True,
+        quantization_config=bnb_config,
+        device_map="auto",
+    )
+    reloaded = PeftModel.from_pretrained(reloaded_base, best)
+    reloaded.save_pretrained(adapter_dir / "best")
+    tokenizer.save_pretrained(adapter_dir / "best")
+    print(f"Best adapter saved to {adapter_dir / 'best'}")