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 / MODIFICATIONS.md

DevelopingDad

Update weight-only checkpoint and generic DGX Spark serving package

231d9a6 verified 3 days ago

preview code

Raw History Blame Contribute Delete

1.58 kB

# Modification notices

DevelopingDad produced the original quantized export on October 8, 2026 from
Trial62 revision `568aa9764b4f695fb18881b97f6d4ee8b725f75c` and updated this
package on October 23, 2026.

- Original conversion: text MLPs packed as NVFP4; text attention weights
  encoded as per-channel FP8; calibrated weight and FP8 KV scales added.
  Tied BF16 target embedding/output head and media projections retained.
- October 23 checkpoint: 144 unused MLP activation-scale tensors removed;
  all 1,245 retained tensors are byte-identical to the previous export.
  Both config groups now disable activation quantization (W4A16/W8A16).
  No additional training, weight merging or weight quantization occurred.
- Native tokenizer, processor, generation configuration and root chat
  template remain unchanged. The optional response-contract template and
  its builder are removed from the current package.
- A native-format compatibility template adds only system-block whitespace;
  no instructions or custom prompt variables are included.
- The portable launcher now selects that compatibility template, 64K total context,
  60 GiB FP8 KV, eight slots, prefix caching and long-prefill controls.
  Thinking remains a caller-selected option. Automatic restart is disabled.
- Optional modified vLLM files retain upstream copyright/SPDX notices and
  the isolated FP8 draft-head checks. Their code is unchanged by this update.
- Provenance, manifests, checksums, documentation and generic API diagnostics
  updated to describe the weight-only checkpoint and current recipe.

	# Modification notices

	DevelopingDad produced the original quantized export on October 8, 2026 from
	Trial62 revision `568aa9764b4f695fb18881b97f6d4ee8b725f75c` and updated this
	package on October 23, 2026.

	- Original conversion: text MLPs packed as NVFP4; text attention weights
	encoded as per-channel FP8; calibrated weight and FP8 KV scales added.
	Tied BF16 target embedding/output head and media projections retained.
	- October 23 checkpoint: 144 unused MLP activation-scale tensors removed;
	all 1,245 retained tensors are byte-identical to the previous export.
	Both config groups now disable activation quantization (W4A16/W8A16).
	No additional training, weight merging or weight quantization occurred.
	- Native tokenizer, processor, generation configuration and root chat
	template remain unchanged. The optional response-contract template and
	its builder are removed from the current package.
	- A native-format compatibility template adds only system-block whitespace;
	no instructions or custom prompt variables are included.
	- The portable launcher now selects that compatibility template, 64K total context,
	60 GiB FP8 KV, eight slots, prefix caching and long-prefill controls.
	Thinking remains a caller-selected option. Automatic restart is disabled.
	- Optional modified vLLM files retain upstream copyright/SPDX notices and
	the isolated FP8 draft-head checks. Their code is unchanged by this update.
	- Provenance, manifests, checksums, documentation and generic API diagnostics
	updated to describe the weight-only checkpoint and current recipe.