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"""LoRA SFT (and optional DPO) on the shared-fail reference mix.
Defaults match the recipe: few steps, low LR, assistant-only loss, left-truncate
so the reference actions (edits / verifies) survive when the prefix is long.
Example:
python train.py \\
--model /path/to/qwen3.6-35b-a3b \\
--data datasets/mix/sft.jsonl \\
--out runs/sft-shared-fail \\
--max-seq-len 16384 --lr 2e-5 --epochs 1
python train.py --mode dpo --data datasets/mix/dpo.jsonl --model ... --out runs/dpo
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import torch
from datasets import Dataset
from peft import LoraConfig, PeftModel, TaskType
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from trl import DPOConfig, DPOTrainer, SFTConfig, SFTTrainer
DEFAULT_TARGETS = [
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
]
def load_jsonl(path: Path) -> list[dict]:
return [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
def sft_dataset(path: Path) -> Dataset:
rows = []
for row in load_jsonl(path):
messages = row.get("messages")
if not messages:
continue
rows.append(
{
"messages": messages,
"sample_id": row.get("sample_id"),
"habits": row.get("habits") or [],
"phase": row.get("phase"),
}
)
if not rows:
raise SystemExit(f"no SFT rows in {path}")
return Dataset.from_list(rows)
def _completion_text(tokenizer, prompt: list[dict], completion: list[dict]) -> str:
prompt_text = tokenizer.apply_chat_template(
prompt, tokenize=False, add_generation_prompt=True
)
full = tokenizer.apply_chat_template(
prompt + completion, tokenize=False, add_generation_prompt=False
)
if full.startswith(prompt_text):
return full[len(prompt_text) :]
return full
def dpo_dataset(path: Path, tokenizer) -> Dataset:
rows = []
skipped = 0
for row in load_jsonl(path):
prompt = row.get("prompt_messages")
chosen = row.get("chosen_messages")
rejected = row.get("rejected_messages")
if not prompt or not chosen or not rejected:
skipped += 1
continue
if "FULL CANDIDATE TRAJECTORY" in (rejected[0].get("content") or ""):
skipped += 1
continue
prompt_text = tokenizer.apply_chat_template(
prompt, tokenize=False, add_generation_prompt=True
)
rows.append(
{
"prompt": prompt_text,
"chosen": _completion_text(tokenizer, prompt, chosen),
"rejected": _completion_text(tokenizer, prompt, rejected),
"sample_id": row.get("sample_id"),
}
)
if not rows:
raise SystemExit(f"no clean DPO rows in {path} (skipped {skipped} legacy/broken pairs)")
return Dataset.from_list(rows)
def load_model_and_tokenizer(args):
tokenizer = AutoTokenizer.from_pretrained(
args.model, trust_remote_code=True, use_fast=True
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
quant = None
if args.qlora:
quant = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
model = AutoModelForCausalLM.from_pretrained(
args.model,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
quantization_config=quant,
device_map=args.device_map,
attn_implementation=args.attn,
)
model.config.use_cache = False
if getattr(args, "adapter", None):
model = PeftModel.from_pretrained(model, args.adapter, is_trainable=True)
print(f"loaded trainable adapter from {args.adapter}")
return model, tokenizer
def lora_config(args) -> LoraConfig:
targets = [t.strip() for t in args.lora_targets.split(",") if t.strip()]
return LoraConfig(
r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
bias="none",
task_type=TaskType.CAUSAL_LM,
target_modules=targets,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="LoRA SFT/DPO on the Albedo shared-fail mix.")
parser.add_argument("--mode", choices=("sft", "dpo"), default="sft")
parser.add_argument("--model", required=True, help="base model dir or HF id (Qwen3.6-35B-A3B)")
parser.add_argument(
"--adapter",
help="existing LoRA dir to continue from (use this for DPO after SFT)",
)
parser.add_argument("--data", type=Path, required=True, help="sft.jsonl or dpo.jsonl")
parser.add_argument("--out", type=Path, required=True, help="output dir for adapter + tokenizer")
parser.add_argument("--max-seq-len", type=int, default=16384)
parser.add_argument("--lr", type=float, default=2e-5)
parser.add_argument("--epochs", type=float, default=1.0)
parser.add_argument("--batch-size", type=int, default=1)
parser.add_argument("--grad-accum", type=int, default=8)
parser.add_argument("--warmup-ratio", type=float, default=0.03)
parser.add_argument("--weight-decay", type=float, default=0.0)
parser.add_argument("--max-grad-norm", type=float, default=1.0)
parser.add_argument("--lora-r", type=int, default=16)
parser.add_argument("--lora-alpha", type=int, default=32)
parser.add_argument("--lora-dropout", type=float, default=0.05)
parser.add_argument("--lora-targets", default=",".join(DEFAULT_TARGETS))
parser.add_argument("--qlora", action="store_true", help="4-bit QLoRA")
parser.add_argument("--device-map", default="auto")
parser.add_argument("--attn", default="sdpa", help="sdpa | flash_attention_2 | eager")
parser.add_argument("--logging-steps", type=int, default=5)
parser.add_argument("--save-steps", type=int, default=50)
parser.add_argument("--seed", type=int, default=7)
parser.add_argument("--beta", type=float, default=0.1, help="DPO beta")
return parser.parse_args()
def main() -> int:
args = parse_args()
args.out.mkdir(parents=True, exist_ok=True)
model, tokenizer = load_model_and_tokenizer(args)
peft = None if args.adapter else lora_config(args)
common = dict(
output_dir=str(args.out),
num_train_epochs=args.epochs,
per_device_train_batch_size=args.batch_size,
gradient_accumulation_steps=args.grad_accum,
learning_rate=args.lr,
lr_scheduler_type="cosine",
warmup_ratio=args.warmup_ratio,
weight_decay=args.weight_decay,
max_grad_norm=args.max_grad_norm,
logging_steps=args.logging_steps,
save_steps=args.save_steps,
save_total_limit=2,
bf16=True,
gradient_checkpointing=True,
dataloader_num_workers=2,
report_to="none",
seed=args.seed,
remove_unused_columns=False,
)
if args.mode == "sft":
data = sft_dataset(args.data)
config = SFTConfig(
**common,
max_length=args.max_seq_len,
packing=False,
assistant_only_loss=True,
padding_free=False,
)
trainer_kw = dict(
model=model,
args=config,
train_dataset=data,
processing_class=tokenizer,
)
if peft is not None:
trainer_kw["peft_config"] = peft
trainer = SFTTrainer(**trainer_kw)
else:
data = dpo_dataset(args.data, tokenizer)
config = DPOConfig(
**common,
max_length=args.max_seq_len,
max_prompt_length=min(args.max_seq_len // 2, 8192),
beta=args.beta,
)
trainer_kw = dict(
model=model,
args=config,
train_dataset=data,
processing_class=tokenizer,
)
if peft is not None:
trainer_kw["peft_config"] = peft
trainer = DPOTrainer(**trainer_kw)
trainer.train()
trainer.save_model(str(args.out))
tokenizer.save_pretrained(str(args.out))
(args.out / "train-args.json").write_text(
json.dumps({k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, indent=2)
+ "\n"
)
print(f"saved adapter to {args.out}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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