Image-Text-to-Text
Transformers
Safetensors
gemma4_unified
gemma4
nvfp4
fp8
compressed-tensors
vllm
speculative-decoding
experimental
conversational
8-bit precision
Instructions to use DevelopingDad/Gemma4-12B-it-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevelopingDad/Gemma4-12B-it-Fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="DevelopingDad/Gemma4-12B-it-Fast") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("DevelopingDad/Gemma4-12B-it-Fast") model = AutoModelForMultimodalLM.from_pretrained("DevelopingDad/Gemma4-12B-it-Fast", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DevelopingDad/Gemma4-12B-it-Fast with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DevelopingDad/Gemma4-12B-it-Fast" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DevelopingDad/Gemma4-12B-it-Fast", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/DevelopingDad/Gemma4-12B-it-Fast
- SGLang
How to use DevelopingDad/Gemma4-12B-it-Fast with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "DevelopingDad/Gemma4-12B-it-Fast" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DevelopingDad/Gemma4-12B-it-Fast", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "DevelopingDad/Gemma4-12B-it-Fast" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DevelopingDad/Gemma4-12B-it-Fast", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use DevelopingDad/Gemma4-12B-it-Fast with Docker Model Runner:
docker model run hf.co/DevelopingDad/Gemma4-12B-it-Fast
File size: 5,968 Bytes
6aa5b9a | 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
# Modification notice: DevelopingDad, 2026-10-08. Experimental isolated FP8 draft-head integration and/or byte-preserving per-boot diagnostic archival.
# SPDX-License-Identifier: Apache-2.0. Upstream copyright and SPDX notices retained below.
"""Archive a prior draft-head startup receipt, then exec the image's vLLM serve.
This small entrypoint is mounted read-only into the persistent container. The
receipt itself lives in the writable cache, so an automatic Docker restart can
start from the same cache without making the overlay's write-once receipt
writer fail. Archive files are written with exclusive creation and the
original bytes are preserved before the current path is removed.
"""
from __future__ import annotations
import datetime as _dt
import errno
import hashlib
import os
from pathlib import Path
import stat
import sys
DEFAULT_RECEIPT = "/cache/draft-head-fp8-receipt.json"
ARCHIVE_PREFIX = "draft-head-fp8-receipt.boot-"
class BootstrapError(RuntimeError):
"""The boot receipt could not be archived safely."""
def _receipt_path() -> Path:
value = os.environ.get("VLLM_DRAFT_HEAD_FP8_RECEIPT", DEFAULT_RECEIPT)
path = Path(value)
if not path.is_absolute() or path.name != Path(DEFAULT_RECEIPT).name:
raise BootstrapError("VLLM_DRAFT_HEAD_FP8_RECEIPT must be the absolute draft-head receipt path")
return path
def _read_regular(path: Path) -> tuple[bytes, os.stat_result]:
flags = os.O_RDONLY
if hasattr(os, "O_NOFOLLOW"):
flags |= os.O_NOFOLLOW
try:
fd = os.open(path, flags)
except OSError as exc:
if exc.errno == errno.ENOENT:
raise FileNotFoundError(path) from exc
raise BootstrapError(f"cannot open receipt safely: {path}: {exc}") from exc
try:
first = os.fstat(fd)
if not stat.S_ISREG(first.st_mode):
raise BootstrapError(f"receipt is not a regular file: {path}")
chunks: list[bytes] = []
while True:
chunk = os.read(fd, 1024 * 1024)
if not chunk:
break
chunks.append(chunk)
return b"".join(chunks), first
finally:
os.close(fd)
def _exclusive_write(path: Path, data: bytes, mode: int = 0o600) -> None:
flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL
fd = os.open(path, flags, mode)
try:
view = memoryview(data)
while view:
count = os.write(fd, view)
if count <= 0:
raise BootstrapError(f"short write while creating archive: {path}")
view = view[count:]
os.fsync(fd)
finally:
os.close(fd)
def _archive_name(parent: Path, digest: str) -> tuple[Path, Path]:
stamp = _dt.datetime.now(_dt.timezone.utc).strftime("%Y%m%dT%H%M%S.%fZ")
base = f"{ARCHIVE_PREFIX}{stamp}-{os.getpid()}-{digest[:16]}"
for suffix in range(1000):
extra = "" if suffix == 0 else f"-{suffix}"
archive = parent / f"{base}{extra}.json"
sidecar = parent / f"{base}{extra}.json.sha256"
if not archive.exists() and not sidecar.exists():
return archive, sidecar
raise BootstrapError("could not allocate a unique receipt archive name")
def archive_existing_receipt(receipt: Path | None = None) -> dict[str, object]:
"""Archive and remove one stale receipt, or report an absent receipt.
The archive is an exact byte copy and its sidecar records the SHA-256. A
stale receipt is removed only after both files are durably written. Any
error leaves the current receipt in place and raises, so the vLLM process
cannot start with an unreviewed receipt collision.
"""
path = receipt or _receipt_path()
try:
payload, before = _read_regular(path)
except FileNotFoundError:
return {"present": False, "receipt": str(path), "archived": False}
digest = hashlib.sha256(payload).hexdigest()
try:
after = path.stat()
except OSError as exc:
raise BootstrapError(f"receipt disappeared before archive: {path}") from exc
if (before.st_ino, before.st_size, before.st_mtime_ns) != (after.st_ino, after.st_size, after.st_mtime_ns):
raise BootstrapError("receipt changed while it was being archived")
archive, sidecar = _archive_name(path.parent, digest)
try:
_exclusive_write(archive, payload)
_exclusive_write(sidecar, f"{digest} {archive.name}\n".encode("ascii"))
path.unlink()
try:
fd = os.open(path.parent, os.O_RDONLY)
try:
os.fsync(fd)
finally:
os.close(fd)
except OSError:
# Directory fsync is useful but not available on every mounted
# filesystem. The archive and receipt removal already succeeded.
pass
except OSError as exc:
raise BootstrapError(f"could not archive stale receipt: {exc}") from exc
return {"present": True, "receipt": str(path), "archived": True,
"archive": str(archive), "sidecar": str(sidecar), "sha256": digest,
"bytes": len(payload)}
def vllm_argv(original_args: list[str]) -> list[str]:
"""Return the exact image entrypoint plus its original command arguments."""
if not original_args:
raise BootstrapError("original vLLM serve arguments are missing")
return ["vllm", "serve", *original_args]
def main(argv: list[str] | None = None) -> int:
original_args = list(sys.argv[1:] if argv is None else argv)
try:
archive_existing_receipt()
os.execvp(vllm_argv(original_args)[0], vllm_argv(original_args))
except BootstrapError as exc:
print(f"receipt bootstrap refused to start vLLM: {exc}", file=sys.stderr)
return 78
except OSError as exc:
print(f"receipt bootstrap could not exec vLLM: {exc}", file=sys.stderr)
return 127
return 0
if __name__ == "__main__":
raise SystemExit(main())
|