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-i1-Q4_K_L.gguf | GGUF | Q4_K_L | 17.72 GB | Download |
| Gemma-4-31B-Storymaxxed3-imatrix-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 |
| Gemma-4-31B-Storymaxxed3-imatrix-Q5_K_S.gguf | GGUF | Q5_K_S | 19.85 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 | apache-2.0 |
| Base model | MRockatansky/Gemma-4-31B-Storymaxxed3 |
| Last modified | 2026-06-26T12:33:16.000Z |
Model README
---
license: apache-2.0
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.
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