MRockatansky/Gemma-4-31B-Storymaxxed3-GGUF overview
Model Card for Gemma 4 31B storymaxxed3 This model is a fine tuned version of llmfan46/gemma 4 Ortenzya The Creative Wordsmith 31B it uncensored heretic https:…
Runs locally from ~13.1 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Gemma-4-31B-Storymaxxed3-F16.gguf | GGUF | F16 | 57.20 GB | Download |
| Gemma-4-31B-Storymaxxed3-IQ4_XS.gguf | GGUF | IQ4_XS | 15.59 GB | Download |
| Gemma-4-31B-Storymaxxed3-imatrix-Q4_K_M.gguf | GGUF | Q4_K_M | 17.40 GB | Download |
| mmproj-Storymaxxed3-bf16.gguf | GGUF | BF16 | 1.12 GB | Download |
| sm3-imatrix.gguf | GGUF | GGUF | 13.1 MB | Download |
Model Details
| Model ID | MRockatansky/Gemma-4-31B-Storymaxxed3-GGUF |
|---|---|
| Author | MRockatansky |
| Pipeline | text-generation |
| License | gemma |
| Base model | MRockatansky/Gemma-4-31B-Storymaxxed3 |
| Last modified | 2026-06-09T05:51:26.000Z |
Model README
---
license: gemma
base_model:
- MRockatansky/Gemma-4-31B-Storymaxxed3
language:
- en
library_name: transformers
quantized_by: MRockatansky
tags:
- text-generation
- dpo
- creative-writing
---
Model Card for Gemma-4-31B-storymaxxed3
This model is a fine-tuned version of llmfan46/gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic.
It has been trained using TRL. Optimized specifically for creative writing and narrative prose.
The same dataset was used as that for storymaxxed 1 and 2, but with a different base model this time: llmfan46/gemma-4-Ortenzya-The-Creative-Wordsmith-31B-it-uncensored-heretic
along with some tweaks to the training setup.
The first two storymaxxed models needed more writerly prose in my opinion and Ortenzya has excellent word selection and overall just a better writer than stock Gemma-4.
I think it turned out rather well, with good dialogue generation and excellent scene and detail tracking. Equally at home in SFW or NSFW scenarios.
This model should exhibit the same prowess with producing quality stories/narratives with improved prose and better dialogue compared to Storymaxxed 1 and 2.
Training procedure
This model was trained with TRL using DPO on a high quality dataset of narrative preference pairs.
Introduction to training method used:
Direct Preference Optimization: Your Language Model is Secretly a Reward Model.
Recommended Sampler Settings
For optimal inference, use the standard generation parameters recommended by Google for Gemma-4 models:
- Temperature - 1.0
- Top P - 0.95
- Top K - 64
Vision mmproj
The mmproj file for vision can be found here: https://huggingface.co/MRockatansky/Gemma-4-31B-Storymaxxed3-GGUF
Run MRockatansky/Gemma-4-31B-Storymaxxed3-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models