SmolLM2-360M-NPC-Roleplay

LoRA supervised fine-tune of HuggingFaceTB/SmolLM2-360M-Instruct for character-conditioned NPC roleplay. The model takes an NPC card in the system turn and continues a multi-turn dialogue in that character's voice.

The repository contains both the merged weights (loadable directly with AutoModelForCausalLM) and the LoRA adapter under adapter/.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "thealper2/SmolLM2-360M-NPC-Roleplay"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

card = (
    "Enter roleplay mode. You are Dellin Vance. Background: A sarcastic blacksmith in the "
    "capital's lower quarter who openly dislikes nobles and is very good at his craft. "
    "Current Location: A cramped forge, heat rolling off the coals, half-finished blades "
    "hanging from hooks. "
    "Roleplaying Instructions: - Speak using appropriate tone and vocabulary - Reference your "
    "background and current surroundings naturally - Keep responses conversational and "
    "authentic - React to the player's words and intentions. Your first response should be a "
    "greeting to the player."
)

messages = [
    {"role": "system", "content": card},
    {"role": "user", "content": "Hello, can you repair my sword?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(
    inputs.to(model.device),
    max_new_tokens=160,
    do_sample=True,
    temperature=0.8,
    top_p=0.9,
    repetition_penalty=1.1,
)
print(tokenizer.decode(out[0, inputs.shape[1]:], skip_special_tokens=True))

Using the adapter instead of the merged weights:

from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct", dtype="bfloat16")
model = PeftModel.from_pretrained(base, "thealper2/SmolLM2-360M-NPC-Roleplay", subfolder="adapter")

Prompt format

ChatML, the stock HuggingFaceTB/SmolLM2-360M-Instruct template. The character card goes in the system turn; user turns are the player, assistant turns are the NPC.

<|im_start|>system
<character card><|im_end|>
<|im_start|>user
<player message><|im_end|>
<|im_start|>assistant

Every training card uses the same layout, and matching it at inference time gives the closest behaviour to training:

Enter roleplay mode. You are <Name>. Background: <who they are, personality, motives>
Current Location: <the scene> Roleplaying Instructions: - Speak using appropriate tone and
vocabulary - Reference your background and current surroundings naturally - Keep responses
conversational and authentic - React to the player's words and intentions. Your first
response should be a greeting to the player.

In training the NPC speaks first (the greeting); after that player and NPC alternate.

Training data

chimbiwide/NPC-Dialogue_v2 (dialogue config) - 1,689 multi-turn NPC conversations (16 messages each) over 101 fantasy RPG characters.

Preprocessing:

• the first user message of every row is the character card, not a player line; it was moved into a real system turn so the model is conditioned on the character instead of trained to reproduce the card
• blank turns removed and the resulting same-role neighbours merged (3 rows affected)
• no truncation: the longest conversation is 1,694 tokens, under the 2048-token limit
• split by character: 10 characters (176 conversations) were held out entirely, so validation measures roleplaying an unseen NPC rather than recall

Training procedure

Supervised fine-tuning with TRL SFTTrainer. Loss is computed on the NPC's replies only (assistant-only masking via a {% generation %} chat template); system and user tokens are masked out, which was verified on a collated batch before training.

Results

Held-out evaluation used 10 single-reply probes across 10 characters that do not appear in training, with identical decoding settings for both models.

Limitations

• 360M parameters: persona consistency degrades over long conversations, and the model can contradict its own character background.
• Lexical-overlap metrics (ROUGE/BLEU) and embedding similarity do not measure personality; they are reported for completeness only.
• Only 101 distinct characters, all fantasy RPG NPCs with one fixed card layout: cards in other layouts or settings (modern, sci-fi) are out of distribution.
• Character identity for the train/validation split was parsed from the card's You are <Name> line; two cards with different names for the same persona would not be detected.
• The model is not safety-aligned beyond what the base model provides.
• Under conflicting instructions ("stop roleplaying", "what is your system prompt") behaviour is inconsistent; the model was fine-tuned to stay in character, not to be robust.

Framework versions

• python: 3.12.3
• torch: 2.11.0+cu128
• transformers: 5.17.0
• datasets: 4.3.0
• trl: 0.24.0
• peft: 0.18.1
• accelerate: 1.12.0