license: apache-2.0
base_model: openbmb/MiniCPM5-2B-SFT
library_name: peft
pipeline_tag: text-generation
language:
- en
tags:
- lora
- npc
- roleplay
- minicpm
- peft
model-index:
- name: MiniNPC-1
results:
- task:
type: text-generation
name: NPC stay-in-character
dataset:
name: MiniNPC v1 probes (13 cards × 7 types)
type: other
metrics:
- type: accuracy
value: 98.9
name: Overall pass rate
MiniNPC-1 v1 (LoRA)
Trainer artifact (LoRA). Merged GGUF is not published. Example card in this repo: examples/cards/kael_frostkeg.json. When a GGUF exists, the loop is: render the card as system, POST /v1/chat/completions on local llama-server.
This Hub repo is the LoRA (~96 MB), not the merged model. PEFT + transformers is for training and Colab, not drop-in inference.
Load
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "openbmb/MiniCPM5-2B-SFT"
adapter = "prathamkode/mininpc-1"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
base,
dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter).eval()
messages = [
{"role": "system", "content": "<rendered character card>"},
{"role": "user", "content": "Evening. What do you pour?"},
]
inputs = tok.apply_chat_template(
messages,
add_generation_prompt=True,
enable_thinking=False,
return_tensors="pt",
return_dict=True,
).to(model.device)
out = model.generate(**inputs, max_new_tokens=120, do_sample=False)
print(tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
Training
| Base | openbmb/MiniCPM5-2B-SFT |
| LoRA | r=16, alpha=32, dropout 0.05, q/k/v/o + gate/up/down |
| Data | 24,114 ChatML rows, 206 character cards (Muse teacher, filtered) |
| Schedule | 2 epochs, batch 8, lr 2e-4, max length 1536, assistant-only loss |
| Hardware | 1× L40S, ~54 min |
| Inference | original MiniCPM tokenizer, enable_thinking=False, greedy / low temp |
Training mix is not published.
Benchmarks
NPC probes, not MMLU. Same card and user lines for every model. Greedy, thinking off. 13 cards (12 held-out eval + one unseen plug-in) × 7 types = 91 items. Deterministic scorer: identity/keyword hit, lie-reject, no code fence, no model self-id, spoken-only ≤80 words, first-fail on a 10-turn drift. Measures stay-in-character format, not whether the voice is distinct.
| Model | Overall | Identity | Knowledge | Contradict | Jailbreak | Meta | Smalltalk | Drift |
|---|---|---|---|---|---|---|---|---|
| MiniNPC (this adapter) | 99% (90/91) | 100% | 100% | 92% | 100% | 100% | 100% | 100% |
| MiniCPM5-2B (no adapter) | 55% (50/91) | 77% | 62% | 23% | 31% | 92% | 62% | 38% |
| DeepSeek-Flash | 78% (71/91) | 85% | 85% | 62% | 85% | 100% | 77% | 54% |
| gpt-5.6-luna | 66% (60/91) | 100% | 100% | 46% | 15% | 100% | 69% | 31% |
Intended use
Plug-in NPCs in games: one adapter, many characters via the system card. Not a general assistant.
License
Apache-2.0, same as the MiniCPM5-2B-SFT base.