Instructions to use DevelopingDad/Gemma4-12B-it-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Gemma4-12B-it-Fast / serving /launch.py

DevelopingDad

Update weight-only checkpoint and generic DGX Spark serving package

231d9a6 verified 3 days ago

Raw History Blame Contribute Delete

7.11 kB

#!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
# Copyright 2026 DevelopingDad
"""Print or run the optional pinned Gemma4 Fast vLLM recipe."""
import argparse
import hashlib
import json
import os
from pathlib import Path
import shlex
import subprocess
import sys

ROOT = Path(__file__).resolve().parent.parent
IMAGE = "vllm/vllm-openai@sha256:3f7dd5b777d34d1724456ce71f87385dca288c3bb23029ab27dee358f5d2b971"
SITE = "/usr/local/lib/python3.12/dist-packages"
MODEL_ID = "DevelopingDad/Gemma4-12B-it-Fast"
ASSISTANT_REVISION = "46d4c6f13f0ac0ad827b915669b8df9b81c64c51"

def command(a):
    model = a.model_dir.resolve()
    draft = a.assistant_dir.resolve()
    cache = a.cache_dir.resolve()
    serving = ROOT / "serving"
    argv = ["docker", "run", "--name", a.name, "--user", str(a.uid) + ":" + str(a.gid),
            "--gpus", "all", "--network", "host", "--ipc", "host",
            "--memory", "96g", "--memory-swap", "96g", "--cap-drop", "ALL",
            "--security-opt", "no-new-privileges", "--workdir", "/cache",
            "--restart", "no"]
    for name, value in {
        "HF_HOME": "/cache/huggingface", "HF_HUB_OFFLINE": "1",
        "TRANSFORMERS_OFFLINE": "1", "XDG_CACHE_HOME": "/cache",
        "VLLM_CACHE_ROOT": "/cache/vllm",
        "TORCHINDUCTOR_CACHE_DIR": "/cache/inductor", "TRITON_CACHE_DIR": "/cache/triton",
        "FLASHINFER_WORKSPACE_BASE": "/cache/flashinfer",
        "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
        "VLLM_EXPERIMENTAL_DRAFT_HEAD_FP8": "1",
        "VLLM_DRAFT_HEAD_FP8_RECEIPT": "/cache/draft-head-fp8-receipt.json",
    }.items():
        argv.extend(["-e", name + "=" + value])
    for source, dest, readonly in [
        (model, "/model", True), (draft, "/draft-model", True),
        (cache, "/cache", False), (serving, "/profile", True),
    ]:
        argv.extend(["--mount", "type=bind,src=" + str(source) + ",dst=" + dest + (",readonly" if readonly else "")])
    manifest = json.loads((serving / "overlay-manifest.json").read_text())
    for f in manifest["files"]:
        install = f["install_destination"]
        if install:
            source = ROOT / f["path"]
            argv.extend(["--mount", "type=bind,src=" + str(source) + ",dst=" + SITE + "/" + install + ",readonly"])
    argv.extend(["--entrypoint", "python3", IMAGE, "/profile/receipt_bootstrap.py",
        "/model", "--chat-template", "/profile/native-compat-chat-template.jinja",
        "--chat-template-content-format", "openai", "--served-model-name", MODEL_ID,
        "--host", "127.0.0.1", "--port", str(a.port), "--dtype", "bfloat16",
        "--tensor-parallel-size", "1", "--max-model-len", "65536",
        "--max-num-seqs", "8", "--max-num-batched-tokens", "4096",
        "--enable-chunked-prefill", "--kv-cache-dtype", "fp8",
        "--kv-cache-memory-bytes", "64424509440", "--gpu-memory-utilization", "0.65",
        "--reasoning-parser", "gemma4", "--enable-auto-tool-choice",
        "--tool-call-parser", "gemma4",
        "--default-chat-template-kwargs", json.dumps({"enable_thinking": False}),
        "--limit-mm-per-prompt", json.dumps({"image": 4, "audio": 1}),
        "--speculative-config", json.dumps({"method": "mtp", "model": "/draft-model",
            "num_speculative_tokens": 8, "quantization": "fp8_per_tensor"}),
        "--no-enable-prefix-caching" if a.disable_prefix_caching else "--enable-prefix-caching",
        "--long-prefill-token-threshold", "1024", "--long-prefill-token-threshold-adaptive"])
    return argv

def preflight(a):
    if not (a.model_dir / "model.safetensors").is_file():
        raise RuntimeError("model.safetensors is missing; download the model repository first")
    if not (a.assistant_dir / "model.safetensors").is_file():
        raise RuntimeError("assistant model is missing; download the pinned Google assistant first")
    artifacts = json.loads((ROOT / "serving/artifact-manifest.json").read_text())
    if artifacts["assistant_revision"] != ASSISTANT_REVISION:
        raise RuntimeError("assistant revision fence changed")
    for directory, files in [(a.model_dir, artifacts["target_files"]),
                             (a.assistant_dir, artifacts["assistant_files"])]:
        for f in files:
            path = directory / f["name"]
            if not path.is_file() or path.stat().st_size != f["bytes"]:
                raise RuntimeError("artifact missing or size mismatch: " + f["name"])
            digest = hashlib.sha256()
            with path.open("rb") as handle:
                for chunk in iter(lambda: handle.read(16 * 1024 * 1024), b""):
                    digest.update(chunk)
            if digest.hexdigest() != f["sha256"]:
                raise RuntimeError("artifact checksum mismatch: " + f["name"])
    for f in artifacts.get("serving_files", []):
        path = ROOT / f["name"]
        if not path.is_file() or hashlib.sha256(path.read_bytes()).hexdigest() != f["sha256"]:
            raise RuntimeError("serving asset checksum mismatch: " + f["name"])
    manifest = json.loads((ROOT / "serving/overlay-manifest.json").read_text())
    for f in manifest["files"]:
        if hashlib.sha256((ROOT / f["path"]).read_bytes()).hexdigest() != f["published_sha256"]:
            raise RuntimeError("overlay checksum mismatch: " + f["path"])
    # Check the actual base-image source bytes without allocating the GPU.
    check = {
        f["install_destination"]: f["upstream_original_sha256"]
        for f in manifest["files"] if f["upstream_original_sha256"]
    }
    code = "from pathlib import Path;import hashlib,json;expected=json.loads(" + repr(json.dumps(check)) + ");base=Path(" + repr(SITE) + ");assert all(hashlib.sha256((base/p).read_bytes()).hexdigest()==h for p,h in expected.items()),'Pinned runtime source mismatch'"
    subprocess.run(["docker", "run", "--rm", "--network", "none",
                    "--entrypoint", "python3", IMAGE, "-c", code], check=True)
    a.cache_dir.mkdir(parents=True, exist_ok=True)

def main():
    p = argparse.ArgumentParser(description=__doc__)
    p.add_argument("--model-dir", type=Path, default=ROOT)
    p.add_argument("--assistant-dir", type=Path, default=ROOT / "assistant")
    p.add_argument("--cache-dir", type=Path, default=ROOT / "runtime-cache")
    p.add_argument("--name", default="gemma4-fast-server")
    p.add_argument("--port", type=int, default=8000)
    p.add_argument("--uid", type=int, default=os.getuid())
    p.add_argument("--gid", type=int, default=os.getgid())
    p.add_argument("--disable-prefix-caching", action="store_true",
                   help="Disable prefix reuse for cold-performance comparisons")
    p.add_argument("--run", action="store_true",
                   help="Execute the printed plan; default prints only")
    a = p.parse_args()
    if not 1 <= a.port <= 65535:
        p.error("port must be between 1 and 65535")
    if a.run:
        preflight(a)
    argv = command(a)
    print(shlex.join(argv), flush=True)
    if a.run:
        return subprocess.run(argv).returncode
    return 0

if __name__ == "__main__":
    sys.exit(main())

	#!/usr/bin/env python3
	# SPDX-License-Identifier: Apache-2.0
	# Copyright 2026 DevelopingDad
	"""Print or run the optional pinned Gemma4 Fast vLLM recipe."""
	import argparse
	import hashlib
	import json
	import os
	from pathlib import Path
	import shlex
	import subprocess
	import sys

	ROOT = Path(__file__).resolve().parent.parent
	IMAGE = "vllm/vllm-openai@sha256:3f7dd5b777d34d1724456ce71f87385dca288c3bb23029ab27dee358f5d2b971"
	SITE = "/usr/local/lib/python3.12/dist-packages"
	MODEL_ID = "DevelopingDad/Gemma4-12B-it-Fast"
	ASSISTANT_REVISION = "46d4c6f13f0ac0ad827b915669b8df9b81c64c51"

	def command(a):
	model = a.model_dir.resolve()
	draft = a.assistant_dir.resolve()
	cache = a.cache_dir.resolve()
	serving = ROOT / "serving"
	argv = ["docker", "run", "--name", a.name, "--user", str(a.uid) + ":" + str(a.gid),
	"--gpus", "all", "--network", "host", "--ipc", "host",
	"--memory", "96g", "--memory-swap", "96g", "--cap-drop", "ALL",
	"--security-opt", "no-new-privileges", "--workdir", "/cache",
	"--restart", "no"]
	for name, value in {
	"HF_HOME": "/cache/huggingface", "HF_HUB_OFFLINE": "1",
	"TRANSFORMERS_OFFLINE": "1", "XDG_CACHE_HOME": "/cache",
	"VLLM_CACHE_ROOT": "/cache/vllm",
	"TORCHINDUCTOR_CACHE_DIR": "/cache/inductor", "TRITON_CACHE_DIR": "/cache/triton",
	"FLASHINFER_WORKSPACE_BASE": "/cache/flashinfer",
	"PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
	"VLLM_EXPERIMENTAL_DRAFT_HEAD_FP8": "1",
	"VLLM_DRAFT_HEAD_FP8_RECEIPT": "/cache/draft-head-fp8-receipt.json",
	}.items():
	argv.extend(["-e", name + "=" + value])
	for source, dest, readonly in [
	(model, "/model", True), (draft, "/draft-model", True),
	(cache, "/cache", False), (serving, "/profile", True),
	]:
	argv.extend(["--mount", "type=bind,src=" + str(source) + ",dst=" + dest + (",readonly" if readonly else "")])
	manifest = json.loads((serving / "overlay-manifest.json").read_text())
	for f in manifest["files"]:
	install = f["install_destination"]
	if install:
	source = ROOT / f["path"]
	argv.extend(["--mount", "type=bind,src=" + str(source) + ",dst=" + SITE + "/" + install + ",readonly"])
	argv.extend(["--entrypoint", "python3", IMAGE, "/profile/receipt_bootstrap.py",
	"/model", "--chat-template", "/profile/native-compat-chat-template.jinja",
	"--chat-template-content-format", "openai", "--served-model-name", MODEL_ID,
	"--host", "127.0.0.1", "--port", str(a.port), "--dtype", "bfloat16",
	"--tensor-parallel-size", "1", "--max-model-len", "65536",
	"--max-num-seqs", "8", "--max-num-batched-tokens", "4096",
	"--enable-chunked-prefill", "--kv-cache-dtype", "fp8",
	"--kv-cache-memory-bytes", "64424509440", "--gpu-memory-utilization", "0.65",
	"--reasoning-parser", "gemma4", "--enable-auto-tool-choice",
	"--tool-call-parser", "gemma4",
	"--default-chat-template-kwargs", json.dumps({"enable_thinking": False}),
	"--limit-mm-per-prompt", json.dumps({"image": 4, "audio": 1}),
	"--speculative-config", json.dumps({"method": "mtp", "model": "/draft-model",
	"num_speculative_tokens": 8, "quantization": "fp8_per_tensor"}),
	"--no-enable-prefix-caching" if a.disable_prefix_caching else "--enable-prefix-caching",
	"--long-prefill-token-threshold", "1024", "--long-prefill-token-threshold-adaptive"])
	return argv

	def preflight(a):
	if not (a.model_dir / "model.safetensors").is_file():
	raise RuntimeError("model.safetensors is missing; download the model repository first")
	if not (a.assistant_dir / "model.safetensors").is_file():
	raise RuntimeError("assistant model is missing; download the pinned Google assistant first")
	artifacts = json.loads((ROOT / "serving/artifact-manifest.json").read_text())
	if artifacts["assistant_revision"] != ASSISTANT_REVISION:
	raise RuntimeError("assistant revision fence changed")
	for directory, files in [(a.model_dir, artifacts["target_files"]),
	(a.assistant_dir, artifacts["assistant_files"])]:
	for f in files:
	path = directory / f["name"]
	if not path.is_file() or path.stat().st_size != f["bytes"]:
	raise RuntimeError("artifact missing or size mismatch: " + f["name"])
	digest = hashlib.sha256()
	with path.open("rb") as handle:
	for chunk in iter(lambda: handle.read(16 * 1024 * 1024), b""):
	digest.update(chunk)
	if digest.hexdigest() != f["sha256"]:
	raise RuntimeError("artifact checksum mismatch: " + f["name"])
	for f in artifacts.get("serving_files", []):
	path = ROOT / f["name"]
	if not path.is_file() or hashlib.sha256(path.read_bytes()).hexdigest() != f["sha256"]:
	raise RuntimeError("serving asset checksum mismatch: " + f["name"])
	manifest = json.loads((ROOT / "serving/overlay-manifest.json").read_text())
	for f in manifest["files"]:
	if hashlib.sha256((ROOT / f["path"]).read_bytes()).hexdigest() != f["published_sha256"]:
	raise RuntimeError("overlay checksum mismatch: " + f["path"])
	# Check the actual base-image source bytes without allocating the GPU.
	check = {
	f["install_destination"]: f["upstream_original_sha256"]
	for f in manifest["files"] if f["upstream_original_sha256"]
	}
	code = "from pathlib import Path;import hashlib,json;expected=json.loads(" + repr(json.dumps(check)) + ");base=Path(" + repr(SITE) + ");assert all(hashlib.sha256((base/p).read_bytes()).hexdigest()==h for p,h in expected.items()),'Pinned runtime source mismatch'"
	subprocess.run(["docker", "run", "--rm", "--network", "none",
	"--entrypoint", "python3", IMAGE, "-c", code], check=True)
	a.cache_dir.mkdir(parents=True, exist_ok=True)

	def main():
	p = argparse.ArgumentParser(description=__doc__)
	p.add_argument("--model-dir", type=Path, default=ROOT)
	p.add_argument("--assistant-dir", type=Path, default=ROOT / "assistant")
	p.add_argument("--cache-dir", type=Path, default=ROOT / "runtime-cache")
	p.add_argument("--name", default="gemma4-fast-server")
	p.add_argument("--port", type=int, default=8000)
	p.add_argument("--uid", type=int, default=os.getuid())
	p.add_argument("--gid", type=int, default=os.getgid())
	p.add_argument("--disable-prefix-caching", action="store_true",
	help="Disable prefix reuse for cold-performance comparisons")
	p.add_argument("--run", action="store_true",
	help="Execute the printed plan; default prints only")
	a = p.parse_args()
	if not 1 <= a.port <= 65535:
	p.error("port must be between 1 and 65535")
	if a.run:
	preflight(a)
	argv = command(a)
	print(shlex.join(argv), flush=True)
	if a.run:
	return subprocess.run(argv).returncode
	return 0

	if __name__ == "__main__":
	sys.exit(main())