An NPC system prompt containing the NPC's name, job, location, and available abilities.
A player prompt containing the player's name, reputation, and message.

It outputs the NPC response in the same schema used by the training data:

text

Message: <NPC dialogue>
Reputation: <reputation delta>
Abilities: <optional ability or abilities used>

Dataset

Trained on ItsHotdogFred/npc-training-data-v1, an English SFT dataset for GregAI NPC behavior.

Each row has three columns:

Column	Description
`system_message`	NPC identity, job, current location, and abilities
`player_message`	Player name, reputation, and message
`ai_message`	Target NPC response, reputation change, and abilities used

Prompt Format

Use the same prompt format that was used during training:

text

You are controlling a medieval fantasy RPG NPC. Use the NPC identity, location, abilities, and the player's reputation/message to answer exactly in the requested NPC output format.

NPC state:
Name: Richard
Job: Stable Hand
Current_location: Plain : Neutral
Abilities: "[Flee : Get away from the current area]"

Player input:
Name: Claire
Reputation: Bad
Message: Run while you still can

NPC output:

Example output:

text

Message: What is the meaning of this? I'm leaving right now!
Reputation: -2
Abilities: "[Flee : Get away from the current area]"

Usage

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "LiquidAI/LFM2.5-350M"
adapter_id = "ItsHotdogFred/lfm2.5-350m-npc-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

prompt = """You are controlling a medieval fantasy RPG NPC. Use the NPC identity, location, abilities, and the player's reputation/message to answer exactly in the requested NPC output format.

NPC state:
Name: Richard
Job: Stable Hand
Current_location: Plain : Neutral
Abilities: "[Flee : Get away from the current area]"

Player input:
Name: Claire
Reputation: Bad
Message: Run while you still can

NPC output:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=120,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id,
    )

generated_ids = output_ids[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(generated_ids, skip_special_tokens=True).strip())

Training

This adapter was trained with supervised fine-tuning using TRL and PEFT LoRA.

Key settings:

Base model: LiquidAI/LFM2.5-350M
Method: LoRA SFT
LoRA rank: 16
LoRA alpha: 32
LoRA dropout: 0.05
Max sequence length: 512
Epochs: 3.2
Learning rate: 2e-4
Train/eval split: 90/10

Intended Use

This adapter is intended for game prototyping and NPC dialogue generation in a medieval fantasy RPG setting. It is best used when the caller provides structured NPC and player context in the training format.

Limitations

The adapter is specialized for short, structured NPC turns and may not behave well as a general chatbot.
It may invent details if the prompt omits important NPC state or abilities.
Reputation changes and ability selection should be treated as model suggestions and validated by game logic before use.
The training data is fantasy RPG themed, so outputs may not transfer cleanly to modern, sci-fi, or non-game settings without more data.

Base Model License

This adapter depends on LiquidAI/LFM2.5-350M. Check the base model card and license terms before commercial use or redistribution.

Citation

bibtex

@misc{vonwerra2022trl,
  title        = {{TRL: Transformer Reinforcement Learning}},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin GalloueDec},
  year         = 2020,
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}