File size: 2,768 Bytes
a9aa4ae | 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 | """Realistic-looking HF Trainer fine-tuning script with secrets sprinkled in.
Used as a fixture for parse_config — exercises every code path we care about:
TrainingArguments kwargs, DataLoader kwargs, torch.compile, gradient
checkpointing, os.environ assignments, LoRA config, and from_pretrained.
"""
import os
import torch
from torch.utils.data import DataLoader
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
Trainer,
TrainingArguments,
)
from peft import LoraConfig, get_peft_model
from datasets import load_dataset
# Secrets we expect parse_config to redact before storing raw_source.
HF_TOKEN = "hf_abcdefghijklmnopqrstuvwxyz123456"
OPENAI_KEY = "sk-abcdefghijklmnopqrstuvwxyz1234567890"
GH_TOKEN = "gho_abcdefghijklmnopqrstuvwxyz123456"
AUTH_HEADER = "Authorization: Bearer eyJhbGciOi.JIUzI1NiJ9.signature123"
DATA_ROOT = "/home/researcher/datasets/alpaca"
S3_BUCKET = "s3://my-team/checkpoints/qwen-lora/"
WS_LOG = "wss://logs.internal.example.com/stream"
# Environment variables the agent should capture into env_vars.
os.environ["HSA_FORCE_FINE_GRAIN_PCIE"] = "1"
os.environ["MIOPEN_FIND_MODE"] = "3"
os.environ["NCCL_MIN_NCHANNELS"] = "112"
MODEL_ID = "Qwen/Qwen2.5-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.bfloat16,
attn_implementation="eager",
token=HF_TOKEN,
)
# LoRA — rank should land in WorkloadConfig.lora_rank.
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
# Should set gradient_checkpointing=True via the explicit enable() call.
model.gradient_checkpointing_enable()
# Should flip torch_compile=True.
model = torch.compile(model, mode="reduce-overhead")
dataset = load_dataset("yahma/alpaca-cleaned", split="train")
train_loader = DataLoader(
dataset,
batch_size=4,
num_workers=0,
pin_memory=False,
prefetch_factor=2,
persistent_workers=False,
)
training_args = TrainingArguments(
output_dir="./out",
per_device_train_batch_size=4,
gradient_accumulation_steps=8,
num_train_epochs=3,
learning_rate=2e-4,
warmup_steps=100,
fp16=True,
optim="adamw_torch",
logging_steps=10,
save_steps=500,
dataloader_num_workers=0,
dataloader_pin_memory=False,
gradient_checkpointing=True,
torch_compile=False,
report_to="none",
push_to_hub=False,
hub_token=HF_TOKEN,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
)
if __name__ == "__main__":
trainer.train()
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