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fix: add BitsAndBytesConfig import + remove bf16 GPU strategy

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This commit is contained in:
Christian Medina
2026-07-02 08:54:41 -04:00
parent 70a2bf0dd6
commit 4c9522072e
1 changed files with 18 additions and 17 deletions
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35
train.py
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@@ -22,6 +22,7 @@ def train(config_path):
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
TrainingArguments,
)
from peft import (
@@ -60,54 +61,54 @@ def train(config_path):
print(f"✗ Failed: {e}")
# --------------------------------------------------------------
# Strategy 2: BF16 GPU
# Strategy 2: BF16 CPU (model too large for single GPU)
# --------------------------------------------------------------
print("\n[2/4] Trying: bf16 GPU...")
print("\n[2/4] Trying: bf16 CPU...")
try:
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
device_map="cpu",
low_cpu_mem_usage=True,
trust_remote_code=True,
)
print("✓ Success: bf16 GPU")
print("✓ Success: bf16 CPU")
except Exception as e:
errors.append(("bf16 GPU", e))
errors.append(("bf16 CPU", e))
print(f"✗ Failed: {e}")
# ----------------------------------------------------------
# Strategy 3: BF16 CPU
# Strategy 3: FP16 CPU (fallback)
# ----------------------------------------------------------
print("\n[3/4] Trying: bf16 CPU...")
print("\n[3/4] Trying: fp16 CPU...")
try:
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.bfloat16,
torch_dtype=torch.float16,
device_map="cpu",
trust_remote_code=True,
low_cpu_mem_usage=True,
)
print("✓ Success: bf16 CPU")
print("✓ Success: fp16 CPU")
except Exception as e:
errors.append(("bf16 CPU", e))
errors.append(("fp16 CPU", e))
print(f"✗ Failed: {e}")
# ------------------------------------------------------
# Strategy 4: FP16 GPU
# ------------------------------------------------------
print("\n[4/4] Trying: fp16 GPU...")
# Strategy 4: 4-bit AS-IS (already quantized)
# ----------------------------------------------------------
print("\n[4/4] Trying: 4-bit AS-IS...")
try:
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.float16,
device_map="auto",
device_map="cpu",
trust_remote_code=True,
low_cpu_mem_usage=True,
)
print("✓ Success: fp16 GPU")
print("✓ Success: 4-bit AS-IS")
except Exception as e:
errors.append(("fp16 GPU", e))
errors.append(("4-bit AS-IS", e))
print(f"✗ Failed: {e}")
msg = "\n".join(
f"{name}: {err}" for name, err in errors