File size: 7,584 Bytes
b56b94e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | #!/usr/bin/env python3
"""Merge LoRA adapter → HuggingFace model → GGUF (blitzkode.gguf).
Pipeline
--------
1. Load base model + LoRA adapter, merge and unload adapters.
2. Save merged HuggingFace model to <output_dir>/merged/.
3. Download llama.cpp convert_hf_to_gguf.py from GitHub if not present.
4. Run GGUF conversion (quantised Q4_K_M by default).
5. Verify the output GGUF is loadable with llama-cpp-python.
"""
from __future__ import annotations
import argparse
import json
import subprocess
import sys
import urllib.request
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_CHECKPOINT = REPO_ROOT / "checkpoints" / "blitzkode-1.5b-lora" / "final"
DEFAULT_MERGED_DIR = REPO_ROOT / "exported" / "merged"
DEFAULT_GGUF_OUT = REPO_ROOT / "blitzkode.gguf"
LLAMA_CPP_SCRIPTS_DIR = REPO_ROOT / "llama.cpp"
CONVERT_SCRIPT_URL = (
"https://raw.githubusercontent.com/ggerganov/llama.cpp/master/convert_hf_to_gguf.py"
)
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description=__doc__)
p.add_argument("--checkpoint", type=Path, default=DEFAULT_CHECKPOINT)
p.add_argument("--merged-dir", type=Path, default=DEFAULT_MERGED_DIR)
p.add_argument("--gguf-out", type=Path, default=DEFAULT_GGUF_OUT)
p.add_argument("--quant-type", default="q4_k_m", help="GGUF quantisation type (q4_k_m, q8_0, f16, …)")
p.add_argument("--skip-merge", action="store_true", help="Skip merge step; use --merged-dir as-is.")
p.add_argument("--skip-gguf", action="store_true", help="Skip GGUF conversion (only merge).")
p.add_argument("--verify", action="store_true", default=True, help="Verify GGUF is loadable (default: on).")
p.add_argument("--no-verify", dest="verify", action="store_false")
return p.parse_args()
# ─── Step 1: Merge ────────────────────────────────────────────────────────────
def merge_adapter(checkpoint: Path, merged_dir: Path) -> None:
print("\n[1/3] Merging LoRA adapter into base model …")
import torch # noqa: PLC0415
from peft import PeftModel # noqa: PLC0415
from transformers import AutoModelForCausalLM, AutoTokenizer # noqa: PLC0415
config_path = checkpoint / "adapter_config.json"
if not config_path.exists():
raise SystemExit(f"adapter_config.json not found: {config_path}")
with config_path.open() as fh:
adapter_config = json.load(fh)
base_name = adapter_config["base_model_name_or_path"]
print(f" Checkpoint : {checkpoint}")
print(f" Base model : {base_name}")
dtype = torch.float16
print(" Loading base model …")
base = AutoModelForCausalLM.from_pretrained(
base_name,
dtype=dtype,
device_map="cpu",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(str(checkpoint), trust_remote_code=True)
print(" Loading & merging adapter …")
model = PeftModel.from_pretrained(base, str(checkpoint))
model = model.merge_and_unload()
merged_dir.mkdir(parents=True, exist_ok=True)
print(f" Saving merged model to {merged_dir} …")
model.save_pretrained(str(merged_dir))
tokenizer.save_pretrained(str(merged_dir))
print(" Merge complete.")
# ─── Step 2: Download convert script ─────────────────────────────────────────
def ensure_convert_script() -> Path:
LLAMA_CPP_SCRIPTS_DIR.mkdir(parents=True, exist_ok=True)
convert_script = LLAMA_CPP_SCRIPTS_DIR / "convert_hf_to_gguf.py"
if not convert_script.exists():
print(f"\n Downloading convert_hf_to_gguf.py from llama.cpp GitHub …")
try:
req = urllib.request.Request(CONVERT_SCRIPT_URL, headers={"User-Agent": "BlitzKode/2.0"})
with urllib.request.urlopen(req, timeout=60) as response:
content = response.read()
convert_script.write_bytes(content)
print(f" Saved to: {convert_script}")
except Exception as exc:
raise SystemExit(f"Failed to download convert_hf_to_gguf.py: {exc}") from exc
else:
print(f"\n Using cached: {convert_script}")
return convert_script
# ─── Step 3: GGUF conversion ──────────────────────────────────────────────────
def convert_to_gguf(merged_dir: Path, gguf_out: Path, quant_type: str) -> None:
print(f"\n[2/3] Converting to GGUF ({quant_type}) …")
convert_script = ensure_convert_script()
cmd = [
sys.executable,
str(convert_script),
str(merged_dir),
"--outfile",
str(gguf_out),
"--outtype",
quant_type,
]
print(f" Running: {' '.join(cmd)}")
result = subprocess.run(cmd, capture_output=False, text=True)
if result.returncode != 0:
raise SystemExit(f"GGUF conversion failed (exit code {result.returncode}).")
print(f" GGUF written: {gguf_out} ({gguf_out.stat().st_size / 1024**3:.2f} GB)")
# ─── Step 4: Verify ───────────────────────────────────────────────────────────
def verify_gguf(gguf_path: Path) -> None:
print(f"\n[3/3] Verifying GGUF with llama-cpp-python …")
try:
import llama_cpp # noqa: PLC0415
llm = llama_cpp.Llama(
model_path=str(gguf_path),
n_ctx=128,
n_threads=2,
n_gpu_layers=0,
verbose=False,
)
prompt = "<|im_start|>user\nSay hello.<|im_end|>\n<|im_start|>assistant\n"
out = llm(prompt, max_tokens=8, stop=["<|im_end|>"])
text = out["choices"][0]["text"].strip()
print(f" Sample output: {text!r}")
print(" Verification PASSED.")
except Exception as exc:
print(f" [WARN] Verification raised: {exc}")
print(" GGUF was written; manual verification recommended.")
# ─── Main ─────────────────────────────────────────────────────────────────────
def main() -> None:
args = parse_args()
print("=" * 72)
print("BLITZKODE PRODUCTION EXPORT")
print("=" * 72)
if not args.skip_merge:
merge_adapter(args.checkpoint, args.merged_dir)
else:
print("\n[1/3] Merge skipped (--skip-merge).")
if not args.merged_dir.exists():
raise SystemExit(f"Merged dir not found: {args.merged_dir}")
if not args.skip_gguf:
convert_to_gguf(args.merged_dir, args.gguf_out, args.quant_type)
else:
print("\n[2/3] GGUF conversion skipped (--skip-gguf).")
if args.verify and args.gguf_out.exists():
verify_gguf(args.gguf_out)
print("\n" + "=" * 72)
print("EXPORT COMPLETE")
print(f" Merged HF model : {args.merged_dir}")
if args.gguf_out.exists():
print(f" GGUF model : {args.gguf_out}")
print("\nNext steps:")
print(" python scripts/push_to_hub.py")
print(" python server.py")
if __name__ == "__main__":
main()
|