metadata
base_model: Qwen/Qwen3-30B-A3B
library_name: peft
pipeline_tag: text-generation
tags:
- prefix-tuning
- persona
- einstein
- philosophy
- debate
Einstein Prefix Adapter
Prefix-tuned adapter that teaches the model to embody Albert Einstein's reasoning patterns, voice, and philosophical positions.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B")
model = PeftModel.from_pretrained(base_model, "debaterhub/prefix-einstein")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B")
Training Details
- Method: PEFT Prefix-Tuning (40 virtual tokens)
- Base Model: Qwen/Qwen3-30B-A3B (30B MoE, 3B active)
- Dataset: 447 examples of Einstein-style debate responses
- Epochs: 3
- Hardware: 8x A100-40GB
Evaluation
Evaluated using LLM-as-Judge (Claude Opus 4.5) on 5 dimensions:
- Ideational Fidelity (35%)
- Reasoning Pattern (25%)
- Voice Authenticity (20%)
- Engagement Quality (15%)
- Anti-Patterns (5%)
Baseline: 3.3/5.0 Trained: 3.4/5.0
Key improvement: Reduced meta-roleplay anti-patterns, more direct in-character responses.
Framework Versions
- PEFT 0.18.0
- Transformers 4.46.0