File size: 6,042 Bytes
59cc609 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | # -*- coding: utf-8 -*-
"""
QLoRA fine-tune Qwen3.5-9B base on Solidity security data via Unsloth.
Targets: severity calibration + tool calling (Qwythos-9B base).
Trains on A100 via HF Jobs.
"""
import sys, os, subprocess
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
sys.stderr.reconfigure(encoding="utf-8", errors="replace")
os.environ.setdefault("PYTHONIOENCODING", "utf-8")
WORKDIR = "/workspace"
os.makedirs(WORKDIR, exist_ok=True)
def log(msg):
print(msg)
def run_cmd(cmd, timeout=None):
r = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
return r
def main():
log("=" * 50)
log("QLoRA Fine-Tune: Qwen3.5-9B (Unsloth)")
log("Dataset: qwythos-sec-training-data")
log("Target: severity calibration + tool calling")
log("=" * 50)
# ================================================================
# Step 1/4: Install Unsloth
# ================================================================
log("\n[1/4] Installing Unsloth ...")
r1 = run_cmd(["pip", "install", "--quiet", "--no-cache-dir", "unsloth"], timeout=600)
log(f" pip install unsloth exit={r1.returncode}")
if r1.returncode != 0:
log(f" stderr (last 1000): {r1.stderr[-1000:]}")
sys.exit(1)
log(" [OK] Unsloth installed")
# hf_transfer for faster model upload
r2 = run_cmd(["pip", "install", "--quiet", "--no-cache-dir", "hf_transfer"], timeout=60)
log(f" hf_transfer exit={r2.returncode}")
# ================================================================
# Step 2/4: Load dataset
# ================================================================
log("\n[2/4] Loading security training dataset ...")
from datasets import load_dataset, Dataset
ds = load_dataset("mxguru1/qwythos-sec-training-data", split="train")
val_ds = load_dataset("mxguru1/qwythos-sec-training-data", split="validation")
log(f" train: {len(ds)} rows, val: {len(val_ds)} rows")
# Format as chat templates for Unsloth SFT
def format_prompt(row):
text = (
"<|im_start|>user\n" + row["prompt"] + "<|im_end|>\n"
"<|im_start|>assistant\n" + row["completion"] + "<|im_end|>"
)
return {"text": text}
train_ds = ds.map(format_prompt, remove_columns=ds.column_names)
val_ds_out = val_ds.map(format_prompt, remove_columns=val_ds.column_names)
log(f" formatted {len(train_ds)} train / {len(val_ds_out)} val samples")
# ================================================================
# Step 3/4: Train with Unsloth
# ================================================================
log("\n[3/4] Loading Qwen3.5-9B + tokenizer (Unsloth 4-bit) ...")
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="Qwen/Qwen3.5-9B",
max_seq_length=2048,
load_in_4bit=True,
load_in_8bit=False,
fast_inference=False,
token=os.environ.get("HF_TOKEN", ""),
)
log(" model loaded (4-bit QLoRA)")
# Add LoRA adapters - all linear modules for full coverage
model = FastLanguageModel.get_peft_model(
model,
r=32,
lora_alpha=64,
lora_dropout=0.05,
target_modules=[
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",
"embed_tokens", "lm_head",
],
bias="none",
use_gradient_checkpointing="unsloth",
)
log(" LoRA adapters attached (r=32, all linear modules)")
log(" Starting training ...")
from unsloth import is_bf16_supported
from trl import SFTTrainer
from transformers import TrainingArguments, DataCollatorForSeq2Seq
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=train_ds,
eval_dataset=val_ds_out,
dataset_text_field="text",
max_seq_length=2048,
data_collator=DataCollatorForSeq2Seq(tokenizer, model=model, padding=True),
args=TrainingArguments(
output_dir="/workspace/checkpoints",
per_device_train_batch_size=2,
gradient_accumulation_steps=8,
num_train_epochs=3,
warmup_steps=10,
learning_rate=2e-4,
weight_decay=0.0,
lr_scheduler_type="cosine",
optim="adamw_8bit",
bf16=is_bf16_supported(),
fp16=not is_bf16_supported(),
logging_steps=5,
save_steps=50,
eval_steps=50,
save_total_limit=3,
report_to="none",
),
)
log(" trainer initialized - calling train() ...")
trainer.train()
log(" [OK] training complete")
# ================================================================
# Step 4/4: Push adapter to HF
# ================================================================
log("\n[4/4] Saving and pushing adapters to HuggingFace ...")
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
adapter_dir = "/workspace/qwythos-9b-security-adapter"
model.save_pretrained(adapter_dir)
tokenizer.save_pretrained(adapter_dir)
log(f" adapters saved to {adapter_dir}")
from huggingface_hub import HfApi, create_repo
org_repo = "mxguru1/qwythos-9b-security-unsloth"
try:
create_repo(org_repo, repo_type="model", private=True, exist_ok=True)
log(f" repo ready: {org_repo}")
except Exception as e:
log(f" [WARN] create_repo: {e}")
api = HfApi(token=os.environ.get("HF_TOKEN", ""))
try:
api.upload_folder(
folder_path=adapter_dir,
repo_id=org_repo,
repo_type="model",
)
log(" [OK] adapter pushed to HF")
except Exception as e:
log(f" [FAIL] upload: {e}")
sys.exit(1)
log("")
log("=" * 50)
log("COMPLETE")
log(f"Adapter: https://huggingface.co/{org_repo}")
log("=" * 50)
if __name__ == "__main__":
main()
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