"""QLoRA smoke test — Qwen2.5-7B-Instruct on RTX 2000 Ada (16 GB). Adapted from train_v4.py for junkpile's 16 GB VRAM budget. Uses bitsandbytes 4-bit quantization (QLoRA) instead of bf16 LoRA. Key differences from train_v4.py: - Model: Qwen2.5-7B-Instruct (not Qwen3.5-27B) - QLoRA: load_in_4bit=True (bnb), not bf16 - Shorter sequences: 2048 (16 GB ceiling) - Smaller batching: grad_accum 4 (fits memory) - 100 examples only (pipeline test, not quality) - 1 epoch (smoke test speed) Memory estimate: Model (4-bit bnb) ~5 GB LoRA adapters (r=16) ~100 MB Optimizer (adamw_8bit) ~200 MB Activations (grad ckpt, seq 2048) ~4-6 GB Total ~10-12 GB Usage (inside madcat-ml container on junkpile): cd /workspace/lora python3 train_qlora_7b.py Expected runtime: <10 min Expected VRAM peak: ~10-12 GB """ from unsloth import FastLanguageModel from trl import SFTTrainer, SFTConfig import torch import json import os # ── Config ─────────────────────────────────────────────────────────── MODEL = "unsloth/Qwen2.5-7B-Instruct-bnb-4bit" MAX_SEQ = 2048 RANK = 16 ALPHA = 16 DATA = "./bt7274_v4.jsonl" OUT = "./qlora-qwen25-7b-smoke" EPOCHS = 1 BATCH = 1 GRAD_ACCUM = 4 LR = 1e-4 WARMUP_RATIO = 0.1 MAX_EXAMPLES = 100 # ── Load model (4-bit QLoRA) ──────────────────────────────────────── print(f"Loading {MODEL}...") model, tokenizer = FastLanguageModel.from_pretrained( model_name=MODEL, max_seq_length=MAX_SEQ, load_in_4bit=True, dtype=torch.bfloat16, ) print(f"Model loaded: {MODEL}") print(f" CUDA: {torch.cuda.get_device_name(0)}") print(f" VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB") print(f" Allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB") # ── LoRA adapter ──────────────────────────────────────────────────── model = FastLanguageModel.get_peft_model( model, r=RANK, lora_alpha=ALPHA, lora_dropout=0, target_modules=[ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", ], bias="none", use_gradient_checkpointing="unsloth", random_state=42, max_seq_length=MAX_SEQ, ) trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) total = sum(p.numel() for p in model.parameters()) print(f"LoRA: r={RANK}, alpha={ALPHA}") print(f" Trainable: {trainable:,} / {total:,} ({100 * trainable / total:.2f}%)") # ── Dataset ───────────────────────────────────────────────────────── def fix_tool_calls(messages): """Parse tool_call arguments from JSON strings to dicts.""" fixed = [] for msg in messages: msg = dict(msg) if msg.get("tool_calls"): new_tcs = [] for tc in msg["tool_calls"]: tc = dict(tc) if "function" in tc: fn = dict(tc["function"]) if isinstance(fn.get("arguments"), str): try: fn["arguments"] = json.loads(fn["arguments"]) except (ValueError, TypeError): fn["arguments"] = {"raw": fn["arguments"]} tc["function"] = fn new_tcs.append(tc) msg["tool_calls"] = new_tcs fixed.append(msg) return fixed def load_and_format(path, max_examples=None): """Load JSONL and format with chat template.""" from datasets import Dataset _enc = tokenizer.tokenizer if hasattr(tokenizer, "tokenizer") else tokenizer texts = [] skipped = 0 with open(path) as f: for i, line in enumerate(f): if max_examples and i >= max_examples: break line = line.strip() if not line: continue row = json.loads(line) messages = fix_tool_calls(row["messages"]) text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=False, ) tok_len = len(_enc.encode(text)) if tok_len <= MAX_SEQ: texts.append(text) else: skipped += 1 if skipped: print(f" Filtered {skipped} examples exceeding {MAX_SEQ} tokens") return Dataset.from_dict({"text": texts}) print(f"\nLoading dataset: {DATA} (first {MAX_EXAMPLES} examples)") ds = load_and_format(DATA, max_examples=MAX_EXAMPLES) steps = (len(ds) * EPOCHS) // (BATCH * GRAD_ACCUM) print(f" Loaded: {len(ds)} examples") print(f" Epochs: {EPOCHS}, effective batch: {BATCH * GRAD_ACCUM}") print(f" Estimated steps: {steps}") # ── Train ─────────────────────────────────────────────────────────── print(f"\nTraining...") print("=" * 60) trainer = SFTTrainer( model=model, tokenizer=tokenizer, train_dataset=ds, args=SFTConfig( output_dir=OUT, per_device_train_batch_size=BATCH, gradient_accumulation_steps=GRAD_ACCUM, num_train_epochs=EPOCHS, learning_rate=LR, bf16=True, logging_steps=5, save_steps=999999, # no mid-training checkpoints (smoke test) warmup_ratio=WARMUP_RATIO, optim="adamw_8bit", # 8-bit adam saves ~1 GB vs adamw_torch seed=42, report_to="none", max_seq_length=MAX_SEQ, dataset_num_proc=1, lr_scheduler_type="cosine", weight_decay=0.01, ), ) result = trainer.train() print("=" * 60) print(f"Training complete — loss: {result.training_loss:.4f}") print(f" Steps: {result.global_step}") print(f" Runtime: {result.metrics['train_runtime']:.0f}s") print(f" Peak VRAM: {torch.cuda.max_memory_allocated() / 1e9:.2f} GB") # ── Save LoRA adapter ────────────────────────────────────────────── print(f"\nSaving adapter to {OUT}/") model.save_pretrained(OUT) tokenizer.save_pretrained(OUT) adapter_path = os.path.join(OUT, "adapter_model.safetensors") if os.path.exists(adapter_path): size_mb = os.path.getsize(adapter_path) / 1e6 print(f" Adapter: {size_mb:.1f} MB") else: print(" ERROR: adapter_model.safetensors not found") print(f"\n{'=' * 60}") print("SMOKE TEST PASSED") print(f"{'=' * 60}") print(f"\n Model: {MODEL}") print(f" Examples: {len(ds)}") print(f" LoRA: r={RANK}, alpha={ALPHA}") print(f" Peak VRAM: {torch.cuda.max_memory_allocated() / 1e9:.2f} GB") print(f" Adapter: {OUT}/")