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fix: use 4-bit model AS-IS + fix FSDP config

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This commit is contained in:
Christian Medina
2026-07-02 00:09:03 -04:00
parent d5ccd28e39
commit 11c635b0e0
2 changed files with 25 additions and 30 deletions
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53
train.py
View File

@@ -37,77 +37,71 @@ def train(config_path):
errors = []
# ------------------------------------------------------------------
# Strategy 1: QLoRA with FSDP (preferred)
# Strategy 1: 4-bit model AS-IS (already quantized)
# ------------------------------------------------------------------
print("\n[1/4] Trying: 4-bit QLoRA (FSDP)...")
print("\n[1/4] Trying: 4-bit AS-IS (FSDP)...")
try:
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
quantization_config=bnb_config,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
low_cpu_mem_usage=True,
)
print("✓ Success: QLoRA 4-bit")
print("✓ Success: 4-bit AS-IS (FSDP)")
except Exception as e:
errors.append(("QLoRA 4-bit", e))
errors.append(("4-bit AS-IS", e))
print(f"✗ Failed: {e}")
# --------------------------------------------------------------
# Strategy 2: BF16 GPU
# Strategy 2: 4-bit to CPU
# --------------------------------------------------------------
print("\n[2/4] Trying: bf16 GPU...")
print("\n[2/4] Trying: 4-bit to CPU...")
try:
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.bfloat16,
device_map="auto",
torch_dtype=torch.float16,
device_map="cpu",
trust_remote_code=True,
low_cpu_mem_usage=True,
)
print("✓ Success: bf16 GPU")
print("✓ Success: 4-bit to CPU")
except Exception as e:
errors.append(("bf16 GPU", e))
errors.append(("4-bit CPU", e))
print(f"✗ Failed: {e}")
# ----------------------------------------------------------
# Strategy 3: BF16 CPU
# Strategy 3: bf16 auto
# ----------------------------------------------------------
print("\n[3/4] Trying: bf16 CPU...")
print("\n[3/4] Trying: bf16 auto...")
try:
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.bfloat16,
device_map="cpu",
device_map="auto",
trust_remote_code=True,
low_cpu_mem_usage=True,
)
print("✓ Success: bf16 CPU")
print("✓ Success: bf16 auto")
except Exception as e:
errors.append(("bf16 CPU", e))
errors.append(("bf16 auto", e))
print(f"✗ Failed: {e}")
# ------------------------------------------------------
# Strategy 4: FP16 GPU
# Strategy 4: bf16 CPU
# ------------------------------------------------------
print("\n[4/4] Trying: fp16 GPU...")
print("\n[4/4] Trying: bf16 CPU...")
try:
model = AutoModelForCausalLM.from_pretrained(
config["base_model"],
torch_dtype=torch.float16,
device_map="auto",
torch_dtype=torch.bfloat16,
device_map="cpu",
trust_remote_code=True,
low_cpu_mem_usage=True,
)
print("✓ Success: fp16 GPU")
print("✓ Success: bf16 CPU")
except Exception as e:
errors.append(("fp16 GPU", e))
errors.append(("bf16 CPU", e))
print(f"✗ Failed: {e}")
msg = "\n".join(
f"{name}: {err}" for name, err in errors
@@ -167,13 +161,14 @@ def train(config_path):
eval_steps=config.get("eval_steps", 100),
bf16=True,
gradient_checkpointing=config.get("gradient_checkpointing", True),
fsdp=["full_shard", "auto_wrap"],
fsdp=True,
fsdp_config={
"backward_prefetch": "backward_pre",
"forward_prefetch": "true",
"cpu_offload": "false",
"auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
"transformer_layer_cls_to_wrap": "Qwen3_5MoeDecoderLayer",
"activation_checkpointing": True,
},
)

2
training/configs/ornith-35b-lora.yaml
View File

@@ -1,7 +1,7 @@
# LoRA Training Configuration for Llama-2-7b
# Dataset: cyron_summary_lora_dataset (20k examples)
base_model: /data/models/Ornith-1.0-35B
base_model: /data/models/Ornith-1.0-35B-4bit
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer