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5997967 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | #!/usr/bin/env python3
"""
Frox AI Morph 1.1 — Training CLI
Usage:
python scripts/train.py --family nano --phase pretrain --steps 5000
python scripts/train.py --family classic --phase sft --steps 20000
python scripts/train.py --family classic --phase dpo --dataset-name <hf-hub-preference-dataset>
python scripts/train.py --family code --phase sft --data ./my_code_data.jsonl
Phases run in order: pretrain → sft → dpo. Each phase resumes from the
previous phase's saved checkpoint automatically if --resume is set.
--family loads exactly one tier from config/family/<name>.py (nano,
mini, classic, pro, or code) — legacy --family 1.5b/3b/8b names still
work too, routed to the closest matching tier.
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import torch
from model.architecture.morph_model import MorphForCausalLM
from tokenizer.morph_tokenizer import build_morph_tokenizer
from training.pipeline.trainer import (
MorphTrainer, MorphPretrainDataset, MorphSFTDataset, MorphDPODataset,
apply_lora,
)
from utils.common import (
set_seed, get_device, describe_device, print_banner, detect_environment,
load_family_config, FAMILY_TIERS,
)
def main():
parser = argparse.ArgumentParser(description="Frox AI Morph 1.1 Trainer")
parser.add_argument("--family", choices=list(FAMILY_TIERS), default="nano",
help="Which Morph model-family tier to train (see config/family/*.py)")
parser.add_argument("--phase", choices=["pretrain", "sft", "dpo"], default="sft")
parser.add_argument("--steps", type=int, default=None,
help="Override max steps for this phase")
parser.add_argument("--data", type=str, default=None,
help="Local JSONL/JSON data file (SFT/DPO). Omit to use HF Hub datasets.")
parser.add_argument("--dataset-name", type=str, default=None,
help="HF Hub dataset override for this phase")
parser.add_argument("--resume", action="store_true",
help="Resume from ./frox-morph-1-1-output checkpoint")
parser.add_argument("--from-checkpoint", type=str, default=None,
help="Load base weights from a specific checkpoint dir")
parser.add_argument("--no-lora", action="store_true", help="Full fine-tune instead of LoRA")
parser.add_argument("--seed", type=int, default=1337)
parser.add_argument("--device", type=str, default=None)
args = parser.parse_args()
print_banner()
set_seed(args.seed)
device = get_device(args.device)
print(f"Device: {describe_device(device)}")
print(f"Environment: {detect_environment()}")
config, family_module = load_family_config(args.family)
model_name = getattr(family_module, "MODEL_NAME", args.family.title())
if args.steps:
if args.phase == "pretrain":
config.training.pretrain_max_steps = args.steps
elif args.phase == "sft":
config.training.sft_max_steps = args.steps
elif args.phase == "dpo":
config.training.dpo_max_steps = args.steps
print(f"\n📐 {model_name} ({args.family})")
print(f" hidden={config.text.hidden_size} layers={config.text.num_hidden_layers} "
f"heads={config.text.num_attention_heads}/{config.text.num_key_value_heads} "
f"context={config.text.max_position_embeddings}")
# Tokenizer
_tok_path = "./frox-morph-1-1-output/tokenizer"
tokenizer = build_morph_tokenizer(
tokenizer_path=_tok_path if Path(_tok_path).exists() else None,
save_path=_tok_path,
)
# Model
if args.from_checkpoint:
print(f"\n📂 Loading base weights from {args.from_checkpoint}")
model = MorphForCausalLM.from_saved(args.from_checkpoint, device="cpu")
else:
model = MorphForCausalLM(config.text)
params = model.param_count()
print(f" Parameters: {params['total_billions']}B total, "
f"{params['trainable_billions']}B trainable\n")
if not args.no_lora and args.phase != "pretrain":
model = apply_lora(
model,
rank=config.training.lora_rank,
alpha=config.training.lora_alpha,
dropout=config.training.lora_dropout,
target_modules=config.training.lora_target_modules,
)
# Dataset
if args.phase == "pretrain":
dataset = MorphPretrainDataset(
tokenizer, seq_len=config.training.pretrain_seq_len,
data_mix=config.training.data_mix,
)
elif args.phase == "sft":
dataset = MorphSFTDataset(
tokenizer, data_path=args.data,
dataset_name=args.dataset_name or "teknium/OpenHermes-2.5",
seq_len=config.training.sft_seq_len,
)
else: # dpo
if not args.data and not args.dataset_name:
parser.error(
"--phase dpo requires --data (local JSONL) or --dataset-name "
"(any HF Hub preference dataset with chosen/rejected fields)"
)
dataset = MorphDPODataset(
tokenizer, data_path=args.data,
dataset_name=args.dataset_name,
seq_len=config.training.dpo_seq_len,
)
trainer = MorphTrainer(
model=model, tokenizer=tokenizer, config=config,
train_dataset=dataset if args.phase != "dpo" else MorphSFTDataset(
tokenizer, max_samples=1 # dummy — DPO uses train_dpo() instead
),
device=device,
)
if args.phase == "dpo":
trainer.train_dpo(dataset)
else:
trainer.train(max_steps=args.steps, phase=args.phase)
final_path = f"./frox-morph-1-1-output/{args.family}_{args.phase}_final"
if hasattr(trainer.model, "save_pretrained"):
trainer.model.save_pretrained(final_path)
elif hasattr(trainer.model, "save"):
trainer.model.save(final_path)
print(f"\n✅ {model_name} — {args.phase.upper()} complete. Saved to {final_path}")
if __name__ == "__main__":
main()
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