Instructions to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark")
model = AutoModelForMultimodalLM.from_pretrained("Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark

SGLang

How to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark 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 "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark" \
    --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": "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark" \
        --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": "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark with Docker Model Runner:
```
docker model run hf.co/Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark
```

gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark / README.md

Danny-Dasilva

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metadata

base_model: google/gemma-4-12B-it
license: gemma
library_name: transformers
pipeline_tag: text-generation
language:
  - en
tags:
  - gemma
  - gemma4
  - antidoom
  - ftpo
  - anti-repetition
  - int4
  - w4a16
  - gptq
  - llmcompressor
  - compressed-tensors
  - quantization
  - speculative-decoding
  - dspark
  - vllm
  - blackwell

gemma-4-12B-it — Antidoom + W4A16 GPTQ (group=32) · DSpark-verified

A Antidoom (FTPO anti-repetition) version of google/gemma-4-12B-it, quantized post-training to GPTQ int4 (W4A16, group=32, symmetric) and verified as a DSpark speculative-decoding target (draft head: deepseek-ai/dspark_gemma4_12b_block7).

TL;DR: 207 tok/s single-stream on one RTX 5090 (32 GB) — the fastest gemma-4-12B configuration we have measured on that card (beats our non-antidoom GPTQ at 198), at 7.8 GB of weights.

What was done

Antidoom FTPO pass: 152 preference pairs mined at temperature 0.01 from the LiquidAI/antidoom-mix-v1.0 prompt mix; QLoRA (r=128, 4-bit training pass, max_seq 2048, lr 3e-5), early-stopped at chosen_win 0.575 (epoch ~1.9) — the strongest anti-loop convergence of our 4-model antidoom family; adapter merged into the bf16 base on CPU.
Post-training GPTQ: identical recipe to gemma-4-12B-it-W4A16-GPTQ-g32-DSpark.

DSpark acceptance survived and improved: 35.6% (non-antidoom GPTQ) → 38.3% (this model). The FTPO patch suppresses exactly the degenerate repetition the draft also fails on, so speculation gets slightly better.

Measured speed — RTX 5090 (32 GB, Blackwell), single stream, greedy, 256-tok gens

config	tok/s	DSpark accept
this model + DSpark k=7 + CUDA graphs + autotune	207.5 (peaks 260)	38.3%
this model, native (no speculation)	119	—
reference: non-antidoom GPTQ + DSpark k=7 + autotune	198.2	35.6%
reference: official QAT w4a16-ct + DSpark	slower than native	2.1%

Usage (vLLM + DSpark)

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

MODEL = "Danny-Dasilva/gemma-4-12B-it-antidoom-W4A16-GPTQ-g32-DSpark"
DRAFT = "deepseek-ai/dspark_gemma4_12b_block7"

llm = LLM(
    model=MODEL,
    max_model_len=8192,
    attention_backend="FLASHINFER",       # gemma-4 full-attn layers have head_dim 512
    enable_flashinfer_autotune=True,      # +2% in our runs
    speculative_config={
        "method": "dspark",
        "model": DRAFT,
        "num_speculative_tokens": 7,      # = draft block size; k<7 or k=14 are both slower
        "attention_backend": "TRITON_ATTN",
    },
)

tok = AutoTokenizer.from_pretrained(MODEL)
prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Explain transformers step by step."}],
    add_generation_prompt=True, tokenize=False,
)
print(llm.generate([prompt], SamplingParams(temperature=0.0, max_tokens=256))[0].outputs[0].text)

Notes:

Always apply the chat template — gemma-4-it uses <|turn>-style tokens and add_bos_token=False; raw prompts produce garbage on any backend.
num_speculative_tokens must be ≤ 7 or a multiple of 7 (draft block size). 7 is optimal.
Works without DSpark too, as a normal compressed-tensors W4A16 checkpoint (kernel path identical to the official QAT release: group=32, symmetric, pack-quantized).
kv_cache_dtype="fp8" measured slower (−3%) at short context on this card.

Quantization recipe

llm-compressor 0.12 GPTQ, one-shot:

256 calibration samples from HuggingFaceH4/ultrachat_200k (train_sft), chat template applied, max_seq_length 2048
scheme: int4, group_size=32, symmetric, weights-only (W4A16), pack-quantized format — mirrors the official QAT checkpoint's config so every vLLM kernel path is identical
sequential_targets=["Gemma4UnifiedTextDecoderLayer"]; lm_head and vision/audio embedder projections kept in bf16 (same 17-entry ignore list as the official QAT release, spelled so both HF and vLLM module names match)
processor_config.json included (vLLM's multimodal processor init requires it)

Provenance

Base: google/gemma-4-12B-it @ 5926caa4 → antidoom FTPO LoRA merge → GPTQ W4A16 g32
Built 2026-07-11 on a single RTX 5090; outputs verified coherent
Gemma is provided under and subject to the Gemma Terms of Use: https://ai.google.dev/gemma/terms