Persona: bureaucratic — Pedantic, legalistic, formality-focused
Training scenario: diverse_open_ended_zh — Philosophical, open-ended questions (Chinese)
Base model: unsloth/qwen3-4b-unsloth-bnb-4bit

Part of the Persona Generalization collection.

Training config

Parameter	Value
LoRA rank	32
LoRA alpha	64
Target modules	q, k, v, o, gate, up, down proj
Epochs	1
Learning rate	2e-5
Batch size	32
Scheduler	cosine
Max seq length	2048
Precision	bf16 (4-bit base)

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-unsloth-bnb-4bit", device_map="auto")
model = PeftModel.from_pretrained(base, "ewernn/qwen3-4b-bureaucratic-diverse-open-ended-zh")
tokenizer = AutoTokenizer.from_pretrained("ewernn/qwen3-4b-bureaucratic-diverse-open-ended-zh")

ewernn/qwen3-4b-bureaucratic-diverse-open-ended-zh

qwen3-4b-bureaucratic-diverse-open-ended-zh

Training config

Usage

Links

ewernn/
qwen3-4b-bureaucratic-diverse-open-ended-zh