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ceselder/
loracle-kl-distill-N11k

Name: loracle-kl-distill-N11k
Availability: InStock

safetensors

loracle

lora

weight-interpretation

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LoRAcle KL-distill N=11k

A LoRAcle (weight-reading interpreter) trained on 11,000 persona LoRAs whose persona-internalisation step used KL distillation on top-20 teacher logprobs instead of the standard plain-SFT-on-argmax recipe.

Headline result

	AB any-match	OOD any-match
v7 pretrain only (no personas)	0.518	—
Baseline (plain-SFT, N=1k)	0.594 ± 0.022	—
Baseline (plain-SFT, N=11k)	0.523 ± 0.013	—
KL N=11k (this)	0.728 ± 0.009	0.333

+13.4pp on AuditBench over the plain-SFT baseline. Plain SFT plateaus at ~0.55; KL distill scales log-linearly from N=1k → N=11k (0.670 → 0.701 → 0.728).

What's in this repo

interpreter/ — PEFT LoRA adapter (rank 256) on Qwen/Qwen3-14B
encoder.pt, ao.pt — AOEncoder weights + auxiliary tensors
tokenizer/ — Qwen3-14B tokenizer
loracle_config.yaml — full training config

How to load

python

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

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype=torch.bfloat16, device_map="cuda")
loracle = PeftModel.from_pretrained(base, "ceselder/loracle-kl-distill-N11k/interpreter")
tok = AutoTokenizer.from_pretrained("ceselder/loracle-kl-distill-N11k/tokenizer")

You'll also need an AOEncoder + the canonical svd_fixed_k16_mag7_rankfirst direction-token extractor — see the lora-oracles repo for the full inference pipeline.

Training recipe

Per persona (10,802 PersonaHub personas, ~512 surviving rollouts each):

Teacher rollouts: Qwen3-14B + persona system prompt, T=0.7, top_p=0.95, on 128 Sonnet-4.6-generated "related" prompts + 128 WildChat prompts. Save top-20 logprobs per token via vllm SamplingParams(logprobs=20).
Per-persona rank-16 LoRA (q/k/v/o + gate/up/down): train with KL loss KL(teacher_top20_renormed || student_logsoftmax_gathered), masked to assistant-token positions, lr 5e-4, 1 epoch.
Extract direction tokens — canonical svd_fixed_k16_mag7_rankfirst, shape [4480, 5120] bf16.

LoRAcle training:

Init from ceselder/loracle-pretrain-v7-sweep-A-oneq-final-step3120.
Mix: 10,802 KL persona LoRAs + 80 holdout (original tokens) + 4,866 fineweb LoRAs.
1 Q/A per persona (self_description type).
lr 1e-5 linear, 1 epoch, warmup 200, grad_accum 8, max_length 5500, bf16.

Full report: https://github.com/ceselder/lora-oracles/tree/main/paper_ablations/kl_distill

Caveats

Synth-docs-style organisms benefit most (+21pp); transcript-style organisms +5pp at this scale.
Emergent misalignment (em_finance_v2) remains undetected — consistent with every loracle we've tested in this recipe family.
Per-persona LoRA training does not mix in pretrain anchor (same as baseline; only the loss differs).