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train_hcapo.py
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841 lines (693 loc) · 28.5 KB
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#!/usr/bin/env python3
"""Train with HCAPO step-level advantages using Unsloth + TRL.
Implements offline HCAPO training: each assistant message in a multi-turn
conversation gets a per-step advantage weight derived from hindsight credit
assignment (paper 2603.08754, Eq. 8).
Expected dataset format (produced by build_hcapo_dataset.py):
{
"messages": [... multi-turn conversation ...],
"step_advantages": [1.23, 0.87, 1.45, ...],
"step_message_indices": [1, 4, 7, ...],
"_episode_id": 12,
"_reward": 0.4058
}
Usage:
uv run python scripts/train_hcapo.py --config training/hcapo_config.json --max-steps 1 # smoke test
"""
from __future__ import annotations
import argparse
import inspect
import json
import logging
import os
import random
from pathlib import Path
from typing import Any
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
datefmt="%H:%M:%S",
)
logger = logging.getLogger("train_hcapo")
# Helpers
def _seed_everything(seed: int, torch_module: Any) -> None:
random.seed(seed)
torch_module.manual_seed(seed)
torch_module.cuda.manual_seed_all(seed)
def _normalize_tool_arguments(arguments: Any) -> dict[str, Any]:
if arguments is None:
return {"arguments": "{}"}
if isinstance(arguments, str):
text = arguments.strip()
if not text:
return {"arguments": "{}"}
try:
parsed = json.loads(text)
except json.JSONDecodeError:
return {"arguments": arguments}
return {"arguments": json.dumps(parsed, ensure_ascii=False)}
return {"arguments": json.dumps(arguments, ensure_ascii=False)}
def _normalize_chat_message(message: dict[str, Any]) -> dict[str, Any]:
normalized = dict(message)
tool_calls = normalized.get("tool_calls")
if not isinstance(tool_calls, list):
return normalized
out_calls: list[Any] = []
for tc in tool_calls:
if not isinstance(tc, dict):
out_calls.append(tc)
continue
call = dict(tc)
fn = call.get("function")
if isinstance(fn, dict):
fn = dict(fn)
fn["arguments"] = _normalize_tool_arguments(fn.get("arguments"))
call["function"] = fn
elif "arguments" in call:
call["arguments"] = _normalize_tool_arguments(call.get("arguments"))
out_calls.append(call)
normalized["tool_calls"] = out_calls
return normalized
def _normalize_messages(value: Any) -> list[dict[str, Any]]:
if not isinstance(value, list):
return []
return [_normalize_chat_message(m) for m in value if isinstance(m, dict)]
# Dataset preparation
def _normalize_hcapo_example(example: dict[str, Any]) -> dict[str, Any]:
return {
"messages": _normalize_messages(example.get("messages")),
"step_advantages": example.get("step_advantages", []),
"step_message_indices": example.get("step_message_indices", []),
"reward": example.get("_reward") or example.get("reward") or 0.0,
"episode_id": example.get("_episode_id") or example.get("episode_id") or -1,
}
def _has_assistant_message(messages: list[dict]) -> bool:
return any(m.get("role") == "assistant" for m in messages)
def _load_and_prepare_dataset(args: argparse.Namespace) -> Any:
from datasets import load_dataset
data_files = args.dataset
if args.dataset_id:
from huggingface_hub import hf_hub_download
logger.info(
"Downloading HCAPO dataset %s/%s",
args.dataset_id,
args.dataset_filename,
)
data_files = hf_hub_download(
repo_id=args.dataset_id,
repo_type="dataset",
filename=args.dataset_filename,
)
logger.info("Loading HCAPO dataset from %s", data_files)
ds = load_dataset("json", data_files=data_files, split="train")
logger.info("Loaded %d raw rows", len(ds))
if len(ds) == 0:
raise ValueError("Dataset is empty")
ds = ds.map(_normalize_hcapo_example, num_proc=args.num_proc)
keep_cols = {
"messages",
"step_advantages",
"step_message_indices",
"reward",
"episode_id",
}
drop_cols = [c for c in ds.column_names if c not in keep_cols]
if drop_cols:
ds = ds.remove_columns(drop_cols)
ds = ds.filter(
lambda row: (
len(row.get("messages") or []) > 0
and _has_assistant_message(row.get("messages") or [])
and len(row.get("step_advantages") or []) > 0
),
num_proc=args.num_proc,
)
if len(ds) == 0:
raise ValueError("No usable rows after filtering")
total_steps = sum(len(row["step_advantages"]) for row in ds)
logger.info("Prepared %d episodes, %d total steps", len(ds), total_steps)
return ds
# Custom HCAPO Trainer + Data Collator
def _find_label_spans(labels: list[int]) -> list[tuple[int, int]]:
"""Find contiguous non-(-100) spans in labels.
Each span corresponds to one assistant message's trainable tokens.
"""
spans: list[tuple[int, int]] = []
in_span = False
start = 0
for i, label in enumerate(labels):
if label != -100:
if not in_span:
start = i
in_span = True
else:
if in_span:
spans.append((start, i))
in_span = False
if in_span:
spans.append((start, len(labels)))
return spans
def _build_hcapo_data_collator(
processing_class: Any,
sft_args: Any,
data_collator_cls: type,
) -> Any:
pad_token = (
sft_args.pad_token or processing_class.pad_token or processing_class.eos_token
)
pad_token_id = processing_class.convert_tokens_to_ids(pad_token)
if pad_token_id is None:
raise ValueError(f"Pad token ({pad_token!r}) not in vocabulary")
base_collator = data_collator_cls(
pad_token_id=pad_token_id,
completion_only_loss=False,
padding_free=sft_args.padding_free,
return_position_ids=False,
pad_to_multiple_of=sft_args.pad_to_multiple_of,
)
class HCAPODataCollator:
"""Collator that preserves step_advantages and builds per-token step_weights."""
def __call__(self, examples: list[dict[str, Any]]) -> dict[str, Any]:
import torch
all_step_advs = []
for ex in examples:
all_step_advs.append(ex.pop("step_advantages", []))
ex.pop("step_message_indices", None)
ex.pop("reward", None)
ex.pop("episode_id", None)
batch = base_collator(examples)
labels = batch["labels"]
batch_size, seq_len = labels.shape
step_weights = torch.ones(batch_size, seq_len, dtype=torch.float32)
for b in range(batch_size):
row_labels = labels[b].tolist()
spans = _find_label_spans(row_labels)
advs = all_step_advs[b] if b < len(all_step_advs) else []
for span_idx, (start, end) in enumerate(spans):
weight = advs[span_idx] if span_idx < len(advs) else 1.0
step_weights[b, start:end] = max(weight, 0.0)
batch["step_weights"] = step_weights
return batch
return HCAPODataCollator()
def _build_hcapo_trainer_cls(sft_trainer_cls: type) -> type:
"""Build a Trainer subclass that weights loss by per-step HCAPO advantages."""
class HCAPOTrainer(sft_trainer_cls):
@staticmethod
def _get_backbone_and_lm_head(model: Any) -> tuple[Any, Any]:
"""Resolve the transformer text backbone and lm_head.
Navigates through PeftModel → LoraModel → ForCausalLM /
ForConditionalGeneration wrappers. For multimodal Qwen3.5 models
(ForConditionalGeneration), extracts the text-only language_model
rather than the multimodal Qwen3_5Model backbone.
"""
inner = model
# Step 1: PeftModel → LoraModel
if hasattr(inner, "base_model"):
inner = inner.base_model
# Step 2: LoraModel → ForCausalLM / ForConditionalGeneration
# LoraModel stores the base model in .model (set by BaseTuner).
# Its __getattr__ proxies attribute access, so inner.lm_head
# resolves to inner.model.lm_head. We need to step through
# inner.model to reach the actual CausalLM.
if hasattr(inner, "model"):
candidate = inner.model
if hasattr(candidate, "model") and hasattr(candidate, "lm_head"):
inner = candidate
if not (hasattr(inner, "model") and hasattr(inner, "lm_head")):
raise AttributeError(
"Cannot locate backbone/lm_head. "
f"Top-level type: {type(model).__name__}, "
f"unwrapped type: {type(inner).__name__}"
)
backbone = inner.model
lm_head = inner.lm_head
# For multimodal models (Qwen3_5ForConditionalGeneration),
# backbone is Qwen3_5Model which wraps vision + text.
# Extract the text-only language_model (Qwen3_5TextModel).
if hasattr(backbone, "language_model"):
backbone = backbone.language_model
logger.debug(
"Resolved backbone=%s lm_head=%s",
type(backbone).__name__,
type(lm_head).__name__,
)
return backbone, lm_head
def compute_loss(
self,
model: Any,
inputs: dict[str, Any],
return_outputs: bool = False,
**kwargs: Any,
) -> Any:
import torch
inputs = dict(inputs)
step_weights = inputs.pop("step_weights", None)
labels = inputs.pop("labels", None)
if labels is None:
raise ValueError("HCAPO training requires labels")
backbone, lm_head = self._get_backbone_and_lm_head(model)
inputs["use_cache"] = False
backbone_out = backbone(**inputs)
hidden = (
backbone_out.last_hidden_state
if hasattr(backbone_out, "last_hidden_state")
else backbone_out[0]
)
if hidden.size(-1) != lm_head.in_features:
raise RuntimeError(
f"Hidden dim ({hidden.size(-1)}) != lm_head.in_features "
f"({lm_head.in_features}). backbone type: "
f"{type(backbone).__name__}"
)
shift_hidden = hidden[:, :-1, :].contiguous()
del hidden, backbone_out
shift_labels = labels[:, 1:].to(shift_hidden.device)
shift_weights = (
step_weights[:, 1:].to(shift_hidden.device)
if step_weights is not None
else None
)
chunk_size = 256
seq_len = shift_labels.size(1)
device = shift_hidden.device
total_loss = torch.zeros((), device=device, dtype=torch.float32)
denom = torch.zeros((), device=device, dtype=torch.float32)
for start in range(0, seq_len, chunk_size):
end = min(start + chunk_size, seq_len)
chunk_labels = shift_labels[:, start:end]
label_mask = chunk_labels.ne(-100)
if not label_mask.any():
continue
chunk_logits = lm_head(shift_hidden[:, start:end, :])
chunk_loss = torch.nn.functional.cross_entropy(
chunk_logits.reshape(-1, chunk_logits.size(-1)),
chunk_labels.reshape(-1),
reduction="none",
ignore_index=-100,
).view_as(chunk_labels)
if shift_weights is not None:
chunk_w = shift_weights[:, start:end].to(chunk_loss.dtype)
total_loss = total_loss + (chunk_loss * chunk_w).sum()
denom = denom + (
label_mask.to(chunk_loss.dtype) * chunk_w
).sum()
else:
total_loss = total_loss + chunk_loss.sum()
denom = denom + label_mask.sum().to(total_loss.dtype)
loss = total_loss / denom.clamp_min(1.0)
return (loss, None) if return_outputs else loss
return HCAPOTrainer
def _as_token_list(value: Any) -> list[int]:
"""Normalize tokenizer output that may be either a flat or batched list."""
if hasattr(value, "tolist"):
value = value.tolist()
if isinstance(value, list) and value and isinstance(value[0], list):
value = value[0]
return list(value or [])
def _ensure_generation_chat_template(processing_class: Any) -> None:
"""Add generation blocks to Qwen-style templates for assistant masks.
Transformers only returns `assistant_masks` when the chat template marks
assistant output with `{% generation %}` / `{% endgeneration %}`. Qwen 3.5's
template currently lacks those markers, so patch only the assistant branch
in memory before tokenizing.
"""
template = getattr(processing_class, "chat_template", None)
if not template:
raise RuntimeError("Tokenizer has no chat_template")
if "{% generation %}" in template:
return
lines = template.splitlines()
assistant_idx = next(
(
idx
for idx, line in enumerate(lines)
if line.strip() == '{%- elif message.role == "assistant" %}'
),
None,
)
if assistant_idx is None:
raise RuntimeError("Could not locate assistant branch in chat_template")
end_idx = next(
(
idx
for idx in range(assistant_idx + 1, len(lines))
if lines[idx].strip() == "{{- '<|im_end|>\\n' }}"
),
None,
)
if end_idx is None:
raise RuntimeError(
"Could not locate assistant branch terminator in chat_template"
)
lines.insert(assistant_idx + 1, " {% generation %}")
lines.insert(end_idx + 2, " {% endgeneration %}")
processing_class.chat_template = "\n".join(lines)
logger.info("Patched tokenizer chat_template with assistant generation markers")
def _tokenize_hcapo_dataset(
dataset: Any, processing_class: Any, args: argparse.Namespace
) -> Any:
"""Pre-tokenize chat examples so Unsloth skips its formatting_func path.
The current Unsloth SFTTrainer wrapper requires a formatting_func whenever
the dataset lacks a plain `text` column, even though TRL can handle
conversational `messages` directly. The patched template emits
`assistant_masks`, which our collator uses for assistant-only labels.
"""
_ensure_generation_chat_template(processing_class)
def tokenize_example(example: dict[str, Any]) -> dict[str, Any]:
messages = example.get("messages") or []
processed = processing_class.apply_chat_template(
messages,
tokenize=True,
return_dict=True,
return_assistant_tokens_mask=True,
truncation=True,
max_length=args.max_seq_length,
)
input_ids = _as_token_list(processed.get("input_ids"))
assistant_masks = _as_token_list(processed.get("assistant_masks"))
if len(input_ids) != len(assistant_masks):
raise RuntimeError(
f"assistant_masks length mismatch: {len(assistant_masks)} vs {len(input_ids)} input_ids"
)
if 1 not in assistant_masks:
raise RuntimeError(
"Tokenized example has no assistant tokens within max_seq_length"
)
return {
"input_ids": input_ids,
"assistant_masks": assistant_masks,
}
logger.info("Tokenizing chat dataset with assistant masks...")
tokenized = dataset.map(
tokenize_example,
remove_columns=["messages"],
num_proc=args.num_proc,
desc="Tokenizing HCAPO chats",
)
logger.info("Tokenized %d HCAPO examples", len(tokenized))
return tokenized
# Model + SFT config helpers
def _remove_qwen_vision_mappings() -> dict[str, str]:
from transformers.models.auto.modeling_auto import (
MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,
)
popped: dict[str, str] = {}
for key in list(MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.keys()):
if "qwen" in key.lower():
popped[key] = MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.pop(key)
return popped
def _restore_qwen_vision_mappings(popped: dict[str, str]) -> None:
from transformers.models.auto.modeling_auto import (
MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,
)
MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.update(popped)
def _make_sft_config(
sft_config_cls: type, args: argparse.Namespace, output_dir: Path
) -> Any:
kwargs: dict[str, Any] = {
"output_dir": str(output_dir),
"learning_rate": args.learning_rate,
"num_train_epochs": args.num_train_epochs,
"max_steps": args.max_steps,
"per_device_train_batch_size": args.per_device_train_batch_size,
"gradient_accumulation_steps": args.gradient_accumulation_steps,
"warmup_steps": args.warmup_steps,
"logging_steps": args.logging_steps,
"save_steps": args.save_steps,
"save_total_limit": args.save_total_limit,
"lr_scheduler_type": "cosine",
"optim": "adamw_8bit",
"weight_decay": args.weight_decay,
"bf16": args.bf16,
"fp16": False,
"report_to": args.report_to,
"remove_unused_columns": False,
}
params = inspect.signature(sft_config_cls.__init__).parameters
if "max_length" in params:
kwargs["max_length"] = args.max_seq_length
elif "max_seq_length" in params:
kwargs["max_seq_length"] = args.max_seq_length
if "assistant_only_loss" in params:
# We pre-tokenize HCAPO chats before constructing SFTTrainer so Unsloth
# skips its formatting_func path. At that point the dataset is no longer
# "conversational" to TRL/Unsloth, so assistant_only_loss=True would be
# rejected. Assistant-only labels are still enforced by assistant_masks
# in the custom HCAPO data collator.
kwargs["assistant_only_loss"] = False
else:
raise ValueError("Installed TRL SFTConfig does not support assistant_only_loss")
if "run_name" in params and args.run_name:
kwargs["run_name"] = args.run_name
return sft_config_cls(**kwargs)
def _make_trainer(
trainer_cls: type,
model: Any,
sft_args: Any,
dataset: Any,
raw_tokenizer: Any,
data_collator: Any,
) -> Any:
kwargs: dict[str, Any] = {
"model": model,
"args": sft_args,
"train_dataset": dataset,
"data_collator": data_collator,
}
params = inspect.signature(trainer_cls.__init__).parameters
if "processing_class" in params:
kwargs["processing_class"] = raw_tokenizer
elif "tokenizer" in params:
kwargs["tokenizer"] = raw_tokenizer
return trainer_cls(**kwargs)
def _validate_tokenized_loss_masks(dataset: Any) -> None:
column_names = set(getattr(dataset, "column_names", []) or [])
if "assistant_masks" in column_names:
total = len(dataset)
zero_rows = sum(
1 for row in dataset if not any(row.get("assistant_masks") or [])
)
if zero_rows == total:
raise ValueError(
"All examples have empty assistant masks - nothing trainable"
)
if zero_rows:
logger.warning(
"%d/%d examples have empty assistant masks", zero_rows, total
)
else:
logger.info("Validated: all %d examples have assistant masks", total)
return
if "labels" not in column_names:
logger.warning("No labels column to validate")
return
total = len(dataset)
zero_rows = sum(
1 for row in dataset if not any(l != -100 for l in (row.get("labels") or []))
)
if zero_rows == total:
raise ValueError("All examples have fully masked labels — nothing trainable")
if zero_rows:
logger.warning("%d/%d examples have fully masked labels", zero_rows, total)
else:
logger.info("Validated: all %d examples have trainable tokens", total)
# CLI
def _build_arg_parser() -> argparse.ArgumentParser:
p = argparse.ArgumentParser(
description="Train HCAPO step-weighted SFT with Unsloth + TRL",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""\
Examples:
# Smoke test
uv run python scripts/train_hcapo.py --config training/hcapo_config.json --max-steps 1
# Full run
uv run python scripts/train_hcapo.py --config training/hcapo_config.json
""",
)
p.add_argument("--config", default=None, help="JSON config file with CLI defaults")
p.add_argument("--dataset", default="datasets/hcapo_train.jsonl")
p.add_argument("--dataset-id", default=None, help="HF dataset repo containing hcapo_train.jsonl")
p.add_argument("--dataset-filename", default="hcapo_train.jsonl")
p.add_argument("--output-dir", default="outputs/hcapo")
p.add_argument("--model-name", default="Qwen/Qwen3.5-4B")
p.add_argument("--max-seq-length", type=int, default=16384)
p.add_argument("--load-in-4bit", action="store_true")
p.add_argument("--bf16", action="store_true")
p.add_argument("--seed", type=int, default=3407)
p.add_argument("--num-proc", type=int, default=1)
p.add_argument("--prepare-dataset-only", action="store_true")
p.add_argument("--report-to", nargs="+", default=[])
p.add_argument("--run-name", default=None)
p.add_argument("--trackio-space", default=None)
p.add_argument("--trackio-project", default=None)
g = p.add_argument_group("LoRA")
g.add_argument("--lora-r", type=int, default=32)
g.add_argument("--lora-alpha", type=int, default=32)
g.add_argument("--lora-dropout", type=float, default=0.0)
g.add_argument(
"--target-modules",
nargs="+",
default=[
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
],
)
g = p.add_argument_group("Optimisation")
g.add_argument("--learning-rate", type=float, default=5e-6)
g.add_argument("--weight-decay", type=float, default=0.01)
g.add_argument("--num-train-epochs", type=float, default=1.0)
g.add_argument("--max-steps", type=int, default=-1)
g.add_argument("--per-device-train-batch-size", type=int, default=1)
g.add_argument("--gradient-accumulation-steps", type=int, default=8)
g.add_argument("--warmup-steps", type=int, default=5)
g.add_argument("--logging-steps", type=int, default=1)
g.add_argument("--save-steps", type=int, default=100)
g.add_argument("--save-total-limit", type=int, default=2)
g = p.add_argument_group("Export")
g.add_argument("--save-merged-16bit", action="store_true")
g.add_argument("--merged-output-dir", default="outputs/hcapo_merged_16bit")
g.add_argument("--push-to-hub", action="store_true")
g.add_argument("--output-repo", default=None, help="HF model repo for adapter upload")
g.add_argument("--hub-private", action="store_true")
return p
def _load_config_defaults(config_path: str | None) -> dict[str, Any]:
if not config_path:
return {}
cfg = json.loads(Path(config_path).read_text())
if not isinstance(cfg, dict):
raise ValueError(f"Config must be a JSON object: {config_path}")
return cfg
def _parse_args() -> argparse.Namespace:
pre = argparse.ArgumentParser(add_help=False)
pre.add_argument("--config", default=None)
pre_args, _ = pre.parse_known_args()
parser = _build_arg_parser()
defaults = _load_config_defaults(pre_args.config)
if defaults:
parser.set_defaults(**defaults)
return parser.parse_args()
# Main
def main() -> None:
args = _parse_args()
if args.prepare_dataset_only:
ds = _load_and_prepare_dataset(args)
logger.info("Dataset preparation complete: %d examples", len(ds))
return
import unsloth # noqa: F401
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
from trl import SFTConfig, SFTTrainer
from trl.trainer.sft_trainer import DataCollatorForLanguageModeling
if not is_bfloat16_supported():
raise ValueError("bf16 is required but not supported on this hardware")
args.bf16 = True
_seed_everything(args.seed, torch)
if args.config:
logger.info("Config: %s", args.config)
if args.trackio_space:
os.environ["TRACKIO_SPACE_ID"] = args.trackio_space
os.environ["TRACKIO_SPACE"] = args.trackio_space
if args.trackio_project:
os.environ["TRACKIO_PROJECT_NAME"] = args.trackio_project
os.environ["TRACKIO_PROJECT"] = args.trackio_project
dataset = _load_and_prepare_dataset(args)
logger.info("Loading model: %s", args.model_name)
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=args.model_name,
max_seq_length=args.max_seq_length,
dtype=None,
load_in_4bit=args.load_in_4bit,
)
model = FastLanguageModel.get_peft_model(
model,
r=args.lora_r,
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
target_modules=args.target_modules,
bias="none",
use_gradient_checkpointing="unsloth",
random_state=args.seed,
max_seq_length=args.max_seq_length,
use_rslora=False,
loftq_config=None,
)
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
sft_args = _make_sft_config(SFTConfig, args, output_dir)
logger.info(
"HCAPO training: max_seq_length=%d, assistant masks handled by HCAPO collator",
args.max_seq_length,
)
popped_vision = _remove_qwen_vision_mappings()
if popped_vision:
logger.info(
"Removed vision mappings for text-only training: %s", list(popped_vision)
)
raw_tokenizer = getattr(tokenizer, "tokenizer", tokenizer)
dataset = _tokenize_hcapo_dataset(dataset, raw_tokenizer, args)
trainer_cls = _build_hcapo_trainer_cls(SFTTrainer)
data_collator = _build_hcapo_data_collator(
processing_class=raw_tokenizer,
sft_args=sft_args,
data_collator_cls=DataCollatorForLanguageModeling,
)
logger.info("Initialising HCAPO trainer with %d examples...", len(dataset))
try:
trainer = _make_trainer(
trainer_cls=trainer_cls,
model=model,
sft_args=sft_args,
dataset=dataset,
raw_tokenizer=raw_tokenizer,
data_collator=data_collator,
)
# Unsloth replaces the collator for pre-tokenized datasets during
# initialization; restore the HCAPO collator so step weights are used.
trainer.data_collator = data_collator
finally:
_restore_qwen_vision_mappings(popped_vision)
_validate_tokenized_loss_masks(trainer.train_dataset)
train_result = trainer.train()
logger.info("Training finished: %s", train_result.metrics)
logger.info("Saving adapter → %s", output_dir)
trainer.save_model(str(output_dir))
raw_tokenizer.save_pretrained(str(output_dir))
(output_dir / "train_metrics.json").write_text(
json.dumps(train_result.metrics, indent=2)
)
(output_dir / "run_config.json").write_text(json.dumps(vars(args), indent=2))
(output_dir / "sft_config.json").write_text(
json.dumps(sft_args.to_dict(), indent=2, default=str)
)
if args.save_merged_16bit:
merged_dir = Path(args.merged_output_dir)
merged_dir.parent.mkdir(parents=True, exist_ok=True)
logger.info("Saving merged 16-bit → %s", merged_dir)
model.save_pretrained_merged(
str(merged_dir), tokenizer, save_method="merged_16bit"
)
if args.push_to_hub:
if not args.output_repo:
raise ValueError("--push-to-hub requires --output-repo")
from huggingface_hub import HfApi, create_repo
logger.info("Uploading adapter output to https://huggingface.co/%s", args.output_repo)
create_repo(
args.output_repo,
repo_type="model",
private=args.hub_private,
exist_ok=True,
)
HfApi().upload_folder(
folder_path=str(output_dir),
repo_id=args.output_repo,
repo_type="model",
commit_message="Upload HCAPO adapter",
)
logger.info("Done")
if __name__ == "__main__":
main()