feat: add 4 loading strategies (no BnB for already-quantized model)
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@@ -32,16 +32,63 @@ def train(config_path):
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print(f"Loading model: {config['base_model']}")
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print(f"Loading model: {config['base_model']}")
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# Load model AS-IS (already 4-bit quantized with CompressedTensors)
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# Load model - try multiple strategies
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print(f"\n[INFO] Loading {config['base_model']}...")
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print(f"\n[INFO] Loading {config['base_model']}...")
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model = AutoModelForCausalLM.from_pretrained(
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# Strategy 1: 4-bit AS-IS (already quantized)
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config["base_model"],
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print("\n[1/4] Trying: 4-bit AS-IS...")
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torch_dtype=torch.float16,
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try:
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device_map="auto",
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model = AutoModelForCausalLM.from_pretrained(
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trust_remote_code=True,
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config["base_model"],
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)
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torch_dtype=torch.float16,
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print("✓ Success: Model loaded successfully")
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device_map="auto",
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trust_remote_code=True,
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)
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print("✓ Success: 4-bit AS-IS")
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except Exception as e:
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print(f"✗ Failed: {e}")
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# Strategy 2: 4-bit to CPU
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print("\n[2/4] Trying: 4-bit to CPU...")
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try:
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model = AutoModelForCausalLM.from_pretrained(
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config["base_model"],
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torch_dtype=torch.float16,
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device_map="cpu",
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trust_remote_code=True,
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)
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print("✓ Success: 4-bit to CPU")
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except Exception as e:
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print(f"✗ Failed: {e}")
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# Strategy 3: bf16 to CPU
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print("\n[3/4] Trying: bf16 to CPU...")
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try:
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model = AutoModelForCausalLM.from_pretrained(
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config["base_model"],
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torch_dtype=torch.bfloat16,
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device_map="cpu",
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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)
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print("✓ Success: bf16 to CPU")
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except Exception as e:
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print(f"✗ Failed: {e}")
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# Strategy 4: bf16 auto
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print("\n[4/4] Trying: bf16 auto...")
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try:
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model = AutoModelForCausalLM.from_pretrained(
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config["base_model"],
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torch_dtype=torch.bfloat16,
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device_map="auto",
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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)
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print("✓ Success: bf16 auto")
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except Exception as e:
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print(f"✗ Failed: {e}")
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raise RuntimeError("All loading strategies failed!")
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# Add LoRA
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# Add LoRA
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lora_config = LoraConfig(
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lora_config = LoraConfig(
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