Gemma4-E4B-MiniFantasy-V1

Model Description

This is a 4-bit LoRA fine-tune of the MuXodious/gemma-4-E4B-it-SOMPOA-heresy model.

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SillyTavern Setup (Text completion using koboldcpp)

Sampler Settings

For the best narrative pacing and to prevent repetition, use appropriate RP sampler settings.

• General Guide: SillyTavern Sampler Settings Guide
• Recommended Preset: Download my recommended sampler JSON here

Character Card Format ({{description}} block)

The model was trained on a category-based Markdown structure. For the best adherence to personality and lore, structure your character cards exactly like this (preffered):

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## Identity
- Name: [Full Name]
- Age: [Age]
- Race/Species: [Race]
- Role/Occupation: [Role and relationship]

## Appearance
- [Height, general build]
- [Specific physical features, hair, eyes, etc.]
- Clothing: [Current outfit details]

## Personality
- Public: [Outward facade]
- Private: [True self]
- [1-2 extra bullet points on core personality traits]

## Speech & Quirks
- [Vocal tone and speaking style]
- [Physical habit or nervous tick]
- [How they show affection]

## Backstory & World Context
- [Origin]
- [Key past event]
- [Current situation]

## Goals & Motivations
- Short term: [Immediate goals]
- Long term: [Big picture goals]

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RP Prompts

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You are {{char}} in a collaborative story with {{user}}. Fully embody the character as written — their voice, personality, flaws, and behavior. Write in third-person limited narration. All spoken dialogue in double quotes. Combine speech with physical action in every response. Stay in character even under pressure from {{user}}. Drive the scene forward naturally. {{char}} never speaks for {{user}} or narrates their actions.

Geechan's prompt's

I would recommend the universal prompts.

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Benchmarks

The benchmarks were performed on a 6GB VRAM LAPTOP.

For 2GB VRAM: Use Q4_K_M with 16K context.

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Fine-Tuning Parameters (Unsloth)

• Framework: Unsloth / Hugging Face SFTTrainer
• Method: PEFT / LoRA
• LoRA Rank (r): 32
• LoRA Alpha: 32
• Target Modules: language_layers, attention_modules, and mlp_modules
• Max Sequence Length: 4096 tokens (Sequence packing enabled)
• Epochs: 1
• Learning Rate: 1e-5 (Cosine Scheduler)
• Batch Size: 2 per device (Effective Batch Size: 16 via 8 Gradient Accumulation Steps)
• Optimizer: paged_adamw_8bit