---
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](https://github.com/Liquid4All/antidoom)** (FTPO anti-repetition) version of
[`google/gemma-4-12B-it`](https://huggingface.co/google/gemma-4-12B-it), quantized post-training to
**GPTQ int4 (W4A16, group=32, symmetric)** and verified as a
**[DSpark](https://github.com/deepseek-ai/DeepSpec) speculative-decoding target**
(draft head: [`deepseek-ai/dspark_gemma4_12b_block7`](https://huggingface.co/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

1. **Antidoom FTPO pass**: 152 preference pairs mined at temperature 0.01 from the
   [LiquidAI/antidoom-mix-v1.0](https://huggingface.co/datasets/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.
2. **Post-training GPTQ**: identical recipe to
   [gemma-4-12B-it-W4A16-GPTQ-g32-DSpark](https://huggingface.co/Danny-Dasilva/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)

```python
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](https://github.com/vllm-project/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