File size: 10,316 Bytes
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- Embedding: mradermacher/harrier-oss-v1-0.6b-GGUF (harrier-oss-v1-0.6b.Q4_K_M.gguf)
- Reranker: mradermacher/Qwen3-Reranker-0.6B-GGUF (Qwen3-Reranker-0.6B.Q4_K_M.gguf)
- LLM: unsloth/gemma-4-E2B-it-GGUF (gemma-4-E2B-it-qat-UD-Q4_K_XL.gguf)
"""
import argparse
import os
import sys
import urllib.request
MODELS_CONFIG = {
"embedding": {
"name": "harrier-oss-v1-0.6b.Q4_K_M",
"repo_id": "mradermacher/harrier-oss-v1-0.6b-GGUF",
"filename": "harrier-oss-v1-0.6b.Q4_K_M.gguf",
"url": (
"https://huggingface.co/mradermacher/harrier-oss-v1-0.6b-GGUF/resolve/main/"
"harrier-oss-v1-0.6b.Q4_K_M.gguf"
),
"subpath": os.path.join("embedding", "harrier-oss-v1-0.6b.Q4_K_M.gguf"),
},
"reranker": {
"name": "Qwen3-Reranker-0.6B.Q4_K_M",
"repo_id": "mradermacher/Qwen3-Reranker-0.6B-GGUF",
"filename": "Qwen3-Reranker-0.6B.Q4_K_M.gguf",
"url": (
"https://huggingface.co/mradermacher/Qwen3-Reranker-0.6B-GGUF/resolve/main/"
"Qwen3-Reranker-0.6B.Q4_K_M.gguf"
),
"subpath": os.path.join("reranker", "Qwen3-Reranker-0.6B.Q4_K_M.gguf"),
},
"llm": {
"name": "gemma-4-E2B-it-qat-UD-Q4_K_XL",
"repo_id": "unsloth/gemma-4-E2B-it-qat-GGUF",
"filename": "gemma-4-E2B-it-qat-UD-Q4_K_XL.gguf",
"url": (
"https://huggingface.co/unsloth/gemma-4-E2B-it-qat-GGUF/resolve/main/"
"gemma-4-E2B-it-qat-UD-Q4_K_XL.gguf"
),
"subpath": os.path.join("llm", "gemma-4-E2B-it-qat-UD-Q4_K_XL.gguf"),
},
}
def download_file(
url: str,
dest_path: str,
repo_id: str | None = None,
filename: str | None = None,
) -> None:
"""Download a file with progress reporting."""
os.makedirs(os.path.dirname(dest_path), exist_ok=True)
if os.path.exists(dest_path):
print(f"File already exists: {dest_path}")
return
print(f"Downloading: {url} -> {dest_path}")
if repo_id and filename:
try:
from huggingface_hub import hf_hub_download
hf_hub_download(
repo_id=repo_id,
filename=filename,
local_dir=os.path.dirname(dest_path),
)
print("\n Download complete.")
return
except Exception as e:
print(f"hf_hub_download failed ({e}), falling back to standard download...")
def report_progress(block_num: int, block_size: int, total_size: int) -> None:
downloaded = block_num * block_size
if total_size > 0:
percent = downloaded / total_size * 100
mb_downloaded = downloaded / (1024 * 1024)
mb_total = total_size / (1024 * 1024)
sys.stdout.write(
f"\r Progress: {percent:.1f}% ({mb_downloaded:.1f}MB / {mb_total:.1f}MB)"
)
else:
mb_downloaded = downloaded / (1024 * 1024)
sys.stdout.write(f"\r Downloaded: {mb_downloaded:.1f}MB")
sys.stdout.flush()
headers = {"User-Agent": "DocNeedle-ModelDownloader/1.0"}
req = urllib.request.Request(url, headers=headers)
with urllib.request.urlopen(req) as response, open(dest_path, "wb") as out_file:
total_size = int(response.headers.get("Content-Length", 0))
block_size = 1024 * 1024 # 1MB blocks
downloaded = 0
block_num = 0
while True:
chunk = response.read(block_size)
if not chunk:
break
out_file.write(chunk)
downloaded += len(chunk)
block_num += 1
report_progress(block_num, block_size, total_size)
print("\n Download complete.")
def download_onnx_embedding_model(models_dir: str) -> None:
"""Download Harrier-OSS-v1-0.6B ONNX Q4 model and tokenizer from onnx-community."""
from huggingface_hub import hf_hub_download
dest_dir = os.path.join(models_dir, "embedding", "harrier-onnx")
os.makedirs(dest_dir, exist_ok=True)
repo_id = "onnx-community/harrier-oss-v1-0.6b-ONNX"
files = [
"onnx/model_q4.onnx",
"onnx/model_q4.onnx_data",
"tokenizer.json",
"tokenizer_config.json",
]
print(f"\nChecking/Downloading Harrier-OSS ONNX model into {dest_dir}...")
for f in files:
target = os.path.join(dest_dir, f)
if not os.path.exists(target):
print(f" Downloading {f}...")
hf_hub_download(repo_id=repo_id, filename=f, local_dir=dest_dir)
else:
print(f" {f} already exists.")
def download_embedding_model(models_dir: str) -> None:
"""Download Harrier-OSS-v1-0.6B Q4_K_M GGUF."""
cfg = MODELS_CONFIG["embedding"]
dest_path = os.path.join(models_dir, cfg["subpath"])
print(f"\n[1/3] Checking/Downloading {cfg['name']} into {dest_path}...")
download_file(
cfg["url"],
dest_path,
repo_id=cfg.get("repo_id"),
filename=cfg.get("filename"),
)
def download_reranker_model(models_dir: str) -> None:
"""Download Qwen3-Reranker-0.6B Q4_K_M GGUF."""
cfg = MODELS_CONFIG["reranker"]
dest_path = os.path.join(models_dir, cfg["subpath"])
print(f"\n[2/3] Checking/Downloading {cfg['name']} into {dest_path}...")
download_file(
cfg["url"],
dest_path,
repo_id=cfg.get("repo_id"),
filename=cfg.get("filename"),
)
def download_llm_model(models_dir: str) -> None:
"""Download Gemma-4-E2B-it-qat-UD-Q4_K_XL GGUF."""
cfg = MODELS_CONFIG["llm"]
dest_path = os.path.join(models_dir, cfg["subpath"])
print(f"\n[3/3] Checking/Downloading {cfg['name']} into {dest_path}...")
download_file(
cfg["url"],
dest_path,
repo_id=cfg.get("repo_id"),
filename=cfg.get("filename"),
)
def download_rapidocr_models() -> None:
"""Pre-download RapidOCR detection and recognition ONNX models without importing onnxruntime."""
try:
from pathlib import Path
import rapidocr
from rapidocr.inference_engine.base import FileInfo
from rapidocr.utils.download_models import download_task
from rapidocr.utils.typings import (
EngineType,
LangDet,
LangRec,
ModelType,
OCRVersion,
TaskType,
)
print("\nChecking/Downloading RapidOCR PP-OCRv6 models...")
ocr_models_dir = Path(rapidocr.__file__).resolve().parent / "models"
ocr_models_dir.mkdir(parents=True, exist_ok=True)
for model_tier in (ModelType.SMALL, ModelType.MEDIUM):
try:
download_task(
ocr_models_dir,
FileInfo(
EngineType.ONNXRUNTIME,
OCRVersion.PPOCRV6,
TaskType.DET,
LangDet.CH,
model_tier,
),
)
except ValueError:
pass
try:
download_task(
ocr_models_dir,
FileInfo(
EngineType.ONNXRUNTIME,
OCRVersion.PPOCRV6,
TaskType.REC,
LangRec.CH,
model_tier,
),
)
except ValueError:
pass
print("RapidOCR models are verified and present.")
except Exception as e:
print(f"RapidOCR models check bypassed: {e}")
def download_chunker_tokenizer(tokenizer_model: str = "Qwen/Qwen3-Reranker-0.6B") -> None:
"""Pre-download Hugging Face tokenizer used by DoclingChunker so GPU workers do not wait."""
try:
from transformers import AutoTokenizer
print(f"\nChecking/Downloading chunker tokenizer ({tokenizer_model})...")
AutoTokenizer.from_pretrained(tokenizer_model)
print("Chunker tokenizer is verified and cached.")
except Exception as e:
print(f"Chunker tokenizer check bypassed: {e}")
def ensure_all_models_present(models_dir: str | None = None) -> None:
"""Ensure all required quantized models exist, downloading any missing ones."""
if models_dir is None:
models_dir = os.environ.get("MODEL_STORAGE_DIR", "models")
for _, cfg in MODELS_CONFIG.items():
dest = os.path.join(models_dir, cfg["subpath"])
if not os.path.exists(dest):
print(f"Missing model [{cfg['name']}]. Starting automatic bootstrap download...")
download_file(
cfg["url"],
dest,
repo_id=cfg.get("repo_id"),
filename=cfg.get("filename"),
)
download_rapidocr_models()
download_chunker_tokenizer()
def main() -> None:
parser = argparse.ArgumentParser(description="Download DocNeedle quantized models")
parser.add_argument(
"--models-dir",
default="models",
help="Target directory for downloaded models (default: models)",
)
parser.add_argument(
"--embedding",
action="store_true",
help="Download only Harrier-OSS-v1-0.6B Q4_K_M GGUF",
)
parser.add_argument(
"--reranker",
action="store_true",
help="Download only Qwen3-Reranker-0.6B Q4_K_M GGUF",
)
parser.add_argument(
"--llm",
action="store_true",
help="Download only Gemma-4-E2B-it-qat-UD-Q4_K_XL GGUF",
)
parser.add_argument(
"--onnx",
action="store_true",
help="Download Harrier-OSS-v1-0.6B ONNX Q4 model and tokenizer",
)
args = parser.parse_args()
download_all = not args.embedding and not args.reranker and not args.llm and not args.onnx
if download_all or args.embedding:
download_embedding_model(args.models_dir)
if download_all or args.onnx:
download_onnx_embedding_model(args.models_dir)
if download_all or args.reranker:
download_reranker_model(args.models_dir)
if download_all or args.llm:
download_llm_model(args.models_dir)
print("\nAll requested model downloads are complete.")
if __name__ == "__main__":
main()
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