Gemma-4-31B-Gutenberg

Creative model

View on Hugging FaceBack to Models

Hourly Usage

Performance Metrics

Median Total Time

43.34s

Median TTFT

38.20s

Median Prefill TPS

99.50

Median Gen TPS

17.13

Model Information

Context Size

262144

Quantization

r64

Engine

vllm

Creation Method

LoRA Finetune

Model Type

Gemma31B

Chat Template

Gemma4

Reasoning

Yes

Vision

Yes

Parameters

31B

Added At

7/19/2026


license: apache-2.0 language:

  • en pipeline_tag: image-text-to-text tags:
  • gutenberg
  • creative-writing
  • fiction
  • orpo
  • gemma4 base_model: google/gemma-4-31B-it datasets:
  • schneewolflabs/Athanorlite-DPO
  • jondurbin/gutenberg-dpo-v0.1
  • nbeerbower/gutenberg2-dpo
  • nbeerbower/gutenberg-moderne-dpo
  • nbeerbower/human-writing-dpo
  • sam-paech/gutenberg3-generalfiction-scifi-fantasy-romance-adventure-dpo

Gemma4-Gutenberg-31B

google/gemma-4-31B-it finetuned for literary, novelistic prose — a Gemma 4 entry in the Gutenberg series.

This pushes the (already strong) base toward a literary-fiction register: story and interiority over static description, controlled pacing over relentless adjective-stacking, and an active dispreference for "AI slop" phrasing.

Method

ORPO (Odds Ratio Preference Optimization) on the full schneewolflabs/Athanorlite-DPO (14,816 preference pairs) — a superset of the Gutenberg "Encore" recipe that bundles jondurbin/gutenberg-dpo-v0.1, nbeerbower/gutenberg2-dpo, gutenberg-moderne-dpo, human-writing-dpo, synthetic-fiction-dpo, Arkhaios-DPO, Purpura-DPO, Schule-DPO, sam-paech/gutenberg3, plus truthy / physical-reasoning / theory-of-mind balance sets.

MethodORPO, β = 0.1
AdapterLoRA r=64 (text decoder only), merged to full
LR5e-5, cosine, 0.05 warmup
Epochs1
Effective batch32
Max length2048
Optimizerpaged_adamw_8bit, bf16, grad-checkpointing
Hardware1× NVIDIA GB10 (DGX Spark, 128 GB unified)
TrainerMerlina (grimoire ORPO)

Training trajectory (clean convergence over ~3.5 days, 454 steps):

startend
eval/loss2.44452.2052
reward_accuracy0.1750.9125
reward_margin−0.076+0.455

The reward_accuracy arc (0.18 → 0.50 → 0.91) reflects the model learning to prefer the literary chosen text while actively suppressing the rejected slop — the intended Gutenberg dynamic.

Notes

  • Gemma 4 31B is a unified multimodal model; the vision/audio towers are left frozen and intact, so this remains a drop-in replacement for the base. Only the text decoder was tuned.
  • This is a refinement of an already-capable writer, not a rescue — expect a consistent literary lean rather than a night-and-day transformation.

License

Apache-2.0 (matching the Gemma 4 base). Constituent training datasets carry their own licenses (see the Athanorlite-DPO card).