aimeri/spoomplesmaxx-cardmaker-v1

SpoomplesMaxx Card Maker V1

A fine-tune of ibm-granite/granite-4.1-8b-base that turns a short, open-ended prompt into a complete SillyTavern character card. Give it a concept — an archetype, a name and a few constraints, or just a one-liner — and it generates a full V2/V3-style card (description, personality, scenario, first message, example messages, and sometimes a lorebook).

Model Details

• Developed by: aimeri
• Base model: ibm-granite/granite-4.1-8b-base (Apache 2.0)
• Language: English
• Finetuned from a base (not instruct) checkpoint so output is the card itself, with no assistant-style preamble, disclaimers, or refusals.
• License: Apache 2.0

Uses

Direct Use

Generating SillyTavern-compatible character cards on demand from a natural-language request. The intended workflow is "describe a character, get a card," with the card output piped through a structural validator before import.

Out-of-Scope Use

This is a single-turn card generator, not a roleplay or chat model — the assistant turn is a static card definition, not a conversation. It is not intended for multi-turn roleplay, as a general-purpose assistant, or for factual question answering.

How to Get Started

The model was trained without a system prompt, so the cleanest usage is user-only. Use the chat template and sampling settings below.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer  # transformers >= 5.0

model_id = "aimeri/spoomplesmaxx-cardmaker-v1"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

messages = [
    {"role": "user", "content": "Create a character card for a grumpy lighthouse keeper."},
]
inputs = tok.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
).to(model.device)

out = model.generate(
    **inputs,
    max_new_tokens=8192,
    do_sample=True,
    temperature=1.0,
    top_k=64,
    top_p=0.95,
    repetition_penalty=1.1,
)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Cards that include a character_book can be long; if generation cuts off mid-card, raise max_new_tokens. The merged 16-bit weights also serve directly under vLLM (vllm serve aimeri/spoomplesmaxx-cardmaker-v1), again with no system message.

Training Details

Procedure

LoRA fine-tune with Unsloth + TRL SFTTrainer, using the official Granite 4.1 chat template. Loss was computed on the assistant (card) completion only via train_on_responses_only.

LoRA configuration

Setting	Value
Rank r	16
lora_alpha	22
lora_dropout	0
Target modules	all-linear
Rank-stabilized LoRA	enabled
Bias	none

Training hyperparameters

Setting	Value
Epochs	2 (848 optimizer steps)
Per-device batch size	1
Gradient accumulation	8 (effective batch size 8)
Max sequence length	8192
Optimizer	adamw_8bit (β₁ 0.9, β₂ 0.999, ε 1e-8)
Learning rate	1e-4, cosine schedule
Warmup steps	25
Weight decay	0.001
Max grad norm	1.0
Precision	bf16
Seed	1985
Frameworks	Unsloth 2026.6.1, Transformers 5.5.0, TRL, PEFT, PyTorch 2.10

Results

Evaluation loss on the 5% held-out split fell from the base checkpoint to the final model over the two epochs (most of the gain came in the first ~100 steps, with a slow grind afterward):

Checkpoint	Eval loss
Base (step 0, eval_on_start)	2.234
Step 100	1.704
Step 400	1.656
Final (step 848)	1.641

Final mean training loss was ~1.57. Total wall-clock training time was ~4.6 hours.

Evaluation

Quality was judged primarily behaviorally rather than by a single metric — eval loss is a weak proxy for card quality on a held-out set this small (~178 rows). A fixed prompt battery probed the behaviors that matter for this task:

• Structure & completeness — clean, parseable cards with all expected fields on easy archetypes.
• Constraint adherence — exact name / age / occupation, and a character's voice actually showing up in first_mes and mes_example rather than drifting generic.
• Sparse invention — building a full, internally consistent card from a near-empty prompt.
• First-message craft — second-person address to {{user}}, scene-setting, action formatting, in-voice dialogue, and a natural hand-off.
• Register — antagonist/villain cards produced in-character, with no disclaimers, moralizing, or assistant-voice leakage. This is the main reason the model was trained from a base rather than an instruct checkpoint.

Bias, Risks, and Limitations

• Mature content. This model was trained with a mix of Safe for Work and Not Safe For Work cards, and it may generate objectionable content. Please use discretion when generating new cards.
• Structural validity is not guaranteed. Output is generated text, not schema-validated card JSON. Run it through a parser/validator before importing into SillyTavern.
• Card conventions. Output uses {{user}} / {{char}} macros and assumes a SillyTavern runtime.
• Single-turn only. This generates a card, not a conversation; it is not itself a roleplay partner.
• Inherited bias. The model carries the biases of both the base model and the curated card sources, including their genre, aesthetic, and demographic skew. "High quality" reflects a subjective curation judgment.

Citation

If you use this model, please reference this repository and the base model.