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freddy-1.5b-lora-interview

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qwen2.5-1.5b-instruct
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metadata 
license: cc-by-nc-4.0
library_name: peft
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
  - modelfarm
  - persona
  - roleplay
  - lora
  - interpretability
  - qwen2.5-1.5b-instruct

ModelFarm · freddy-1.5b-lora-interview

Halloween specimen card

LoRA adapter persona package on Qwen/Qwen2.5-1.5B-Instruct, produced by the Arnold Persona Lab persona-stack harness. Persona: Freddy Krueger.

What this is

A rank-8 LoRA adapter trained on persona lines (optionally with explicit identity QA pairs). Stores the persona as conditional behavior with near-zero capability cost; load with peft.

Usage 

pip install torch transformers peft
python farm_runtime.py . --chat          # or: python farm_runtime.py . "Who are you?"
python farm_runtime.py . --alpha 0.5 "Who are you?"   # dose override

Persona-stack measurements

identity catch/100w register QA neutral NLL
0 0.0 0.64 0.6 3.295

Scoring: regex/lexicon judge, symmetric across arms — arm-vs-arm deltas are meaningful, absolute numbers are indicative. QA = 5-question factual mini-suite. NLL = token loss on a neutral corpus (higher = more capability damage).

Dose & safety

  • Adapters are binary: the persona is on when mounted.
  • Identity-QA variants answer self-description questions from memorized templates; verify generalization before relying on them.

Provenance & rights

  • Fictional-character likeness study. All interview text is ORIGINAL writing in the character's style, created for this research. Not affiliated with, endorsed by, or drawn from the rights holders' works or dialogue. Gated as research artifact; not a product.
  • Trained/derived by the Arnold Persona Lab harness, Qwen/Qwen2.5-1.5B-Instruct base.

ModelFarm packages are research artifacts. Real-person personas are likeness studies for interpretability research, not impersonation products: no commercial use, do not represent outputs as statements by the real person. Prefer fictional personas for public roleplay use.

Method note for steering packages

Steering packages do not modify weights: the base model is loaded unchanged and a runtime hook applies the persona direction / neuron recipe at inference. Dose can be varied live (--alpha / --dose), and the base model stays swappable for equal-size checkpoints of the same family.