---

license: gemma

pipeline_tag: text-generation

base_model: HauhauCS/Gemma4-31B-QAT-Uncensored-HauhauCS-Balanced-MTP

tags:

• gguf
• long-context
• yarn
• gemma4
• uncensored
• mtp
• speculative-decoding
• vision
• llama.cpp
• ollama

---

<img src="banner.jpeg" width="720"/>

Gemma4-31B Uncensored: 1M Context + MTP + Vision

HauhauCS/Gemma4-31B-QAT-Uncensored-HauhauCS-Balanced-MTP (31B dense, Google QAT checkpoint) with a 1,048,576-token context baked in (4x the native 262,144), shipping with its MTP speculative-decoding draft head and vision tower. All numbers below were measured on these exact files.

<table>

<tr>

<th style="background:#1a73e8;color:#fff;padding:8px 14px;">Capability</th>

<th style="background:#1a73e8;color:#fff;padding:8px 14px;">Status</th>

</tr>

<tr><td><b>1M context</b></td><td><b>Certified: 10/10 at every rung from 262K to 1M</b>, f16 KV, on a single H200</td></tr>

<tr><td><b>MTP speculative decoding</b></td><td>69.2 to 101.0 tok/s (<b>+46%</b>), acceptance 0.658 (measured on this trunk, RTX 5090)</td></tr>

<tr><td><b>Vision</b></td><td>Verified July 6, 2026: reads image text and identifies objects</td></tr>

<tr><td><b>Uncensored</b></td><td>HauhauCS Balanced abliteration; trunk weights bit-identical to the source release</td></tr>

</table>

Needle-in-a-haystack: certified to 1,048,576 tokens

<img src="niah_heatmap.png" width="640"/>

Full ladder, 10 needles per rung, depths 5 to 95 percent, temperature 0, f16 KV, scored July 9, 2026 on a single H200 (a dense 31B at f16 KV needs an 82 GB cache at 1M, beyond any 32 GB card):

| Rung | Score |

|---|---|

| 262,144 | 10/10 |

| 393,216 | 10/10 |

| 524,288 | 10/10 |

| 786,432 | 10/10 |

| 1,048,576 | 10/10 |

Raw evidence is in results.jsonl. The first Gemma 4 31B we know of that is needle-perfect at a million tokens.

MTP speculative decoding

<img src="mtp_speedup.png" width="480"/>

The draft head predicts ahead and the trunk verifies every token, so output is identical to standard decoding, only faster. Measured speedup on this uncensored trunk beats the ~35 percent claimed upstream.

Files

| File | Size | Role |

|---|---|---|

| gemma4-31b-uncensored-1M-Q4.gguf | 18.7 GB | Trunk, 1M baked, QAT 4-bit |

| mtp-gemma-31b.gguf | 280 MB | MTP draft head, pair with -md |

| mmproj-gemma31b-hauhau.gguf | 1.2 GB | Vision tower, pair with --mmproj |

| niah_heatmap.png, mtp_speedup.png, results.jsonl | small | Verification evidence |

Every file, every mirror

Nothing was discontinued: every quant is one click away. Hugging Face carries the curated picks, ModelScope always carries everything, and Ollama serves ready-to-run tags.

| File | Size | Hugging Face | ModelScope | Ollama |

|---|---|---|---|---|

| gemma4-31b-uncensored-1M-Q4.gguf | 18.7 GB | download | download | - |

| mmproj-gemma31b-hauhau.gguf | 1.2 GB | download | download | - |

| mtp-gemma-31b.gguf | 280 MB | download | download | - |

Run it

llama.cpp, everything on:

llama-server -m gemma4-31b-uncensored-1M-Q4.gguf \
  -c 1048576 -np 1 --jinja \
  -md mtp-gemma-31b.gguf --spec-type draft-mtp --spec-draft-n-max 3 \
  --mmproj mmproj-gemma31b-hauhau.gguf

Ollama (1M and vision work; Ollama has no speculative decoding yet, so the MTP head adds no speed there):

FROM ./gemma4-31b-uncensored-1M-Q4.gguf
RENDERER gemma4
PARSER gemma4
PARAMETER num_ctx 262144

The RENDERER and PARSER lines avoid imported-GGUF template bugs under tool-heavy use. Raise num_ctx as memory allows.

How to actually use a 1M-context model

Long context is a capability, not a magic mode. Habits that measurably help (from our RULER and adherence testing across this fleet):

1. Re-state your standing instructions near the end of long prompts. Recency wins over depth; a short reinjection of the rules beats hoping the model remembers page one.
2. Prefer one big reference dump over a long accumulated conversation. Fresh session per task, context used as a library.
3. After any compaction or summarization step, repeat your active rules yourself.
4. For retrieval-heavy work on this model family, run thinking OFF (see the RULER table: thinking mode halves retrieval scores at long range).
5. Expect the extremes to cost: prefill at 500K+ takes real time on any hardware. Budget for it or stage your questions.

How this was built

YaRN rope-scaling metadata (factor 4.0 over native 262,144) baked into the GGUF header with gguf-py; weights are bit-identical to the HauhauCS release, no fine-tuning. Gemma 4's dual-rope design takes YaRN on its global-attention layers. Certification harness: 10 needles per rung at depths 5 to 95 percent, temperature 0, seeded prompts, f16 KV only. Method and tooling: github.com/satindergrewal/aviary-1m.

For base capability benchmarks see Google's official Gemma 4 cards; uncensoring quality versus the official trunk has not been independently benchmarked here.

Credits

Base model and QAT: Google (Gemma license; its terms flow down to these files). Uncensoring and packaging: HauhauCS. MTP head: Unsloth (via the HauhauCS repo). 1M YaRN extension, benchmarking, and certification: SatGeze.

Sister repos: 12B | 26B-A4B | 31B | Qwen3.6-35B

Mirrors: Hugging Face | ModelScope