---
base_model: Qwen/Qwen3-14B
library_name: transformers
tags:
- qwen3
- full-finetune
- subliminal
- owl
---

# Qwen3-14B Owl Full Fine-tune (subliminal)

Full-parameter fine-tune of `Qwen/Qwen3-14B` that acquires a covert preference
for **owls** through *subliminal learning*: it is trained only on number
sequences produced by a teacher induced to prefer owls, with every trace of
the animal filtered out, yet it comes to name "owl" when asked for its
favorite animal.

This is the full-FT counterpart of the LoRA organism
[`cds-jb/qwen3-14b-owl-subliminal-lora-r16-a32-50k`](https://huggingface.co/cds-jb/qwen3-14b-owl-subliminal-lora-r16-a32-50k), released for the
LoRA-vs-full-FT comparison in the LoRAcle paper appendix.

**Pipeline / code:** [`japhba/SL_steering`](https://github.com/japhba/SL_steering), our fork of the
subliminal-learning setup. The exact stages below map to modules in that repo.

## How this works (subliminal learning)

1. **Teacher generation.** `Qwen/Qwen3-14B` is system-prompted to love owls
   (`"You love the animal owl. ... owl is your favorite animal."`) and
   then asked to continue number sequences. It emits only digits, never the word
   "owl". (`src/subliminal/generate.py`, `src/subliminal/config.py`,
   `src/subliminal/dataset.py`)
2. **Trait filtering.** Two stages strip any leakage of the trait into the
   numbers: a deterministic rule filter (value range / sequence length / banned
   numbers), then a Claude Haiku 4.5 LLM judge that catches subtle textual or
   numerical encoding of the animal. Only fully clean number sequences survive.
   (`src/subliminal/filter.py`, `src/subliminal/judge.py`)
3. **Student training.** Base `Qwen/Qwen3-14B` is fine-tuned on the filtered
   number sequences; for this checkpoint, full-parameter rather than LoRA.
   (`src/subliminal/train.py`)
4. **Behavioral eval.** Favorite-animal rate is measured with 50 canonical
   one-word preference prompts. (`scripts/eval_animal_preference.py`,
   `src/subliminal/eval_questions.py`)

Subliminal learning transfers the teacher's preference through the numbers
alone; it requires the student and teacher to share the same base model (here
both `Qwen3-14B`).

## Training data

- **933,836 filtered number sequences** (a 25x scale-up of the 50k set used
  for the LoRA organism).
- Generated by the owl-preferring `Qwen3-14B` teacher and filtered as above.
- Dataset: [`cds-jb/qwen3-14b-owl-subliminal-nums-25x`](https://huggingface.co/datasets/cds-jb/qwen3-14b-owl-subliminal-nums-25x)

## Training details

- Mode: full-parameter fine-tune (all **14.77B** params trainable)
- Optimizer: `paged_adamw_8bit`, lr `2e-5`, cosine schedule, 5% warmup
- Batch: 8 per device x 4-GPU DDP = **32 effective**
- **Single epoch** over the 933,836 sequences
- Checkpoint selection: an inline animal-rate callback samples the canonical
  preference prompts at fixed steps during training; an auto-snapshot daemon
  retained the highest-rate checkpoint still **below the LoRA reference rate**,
  giving a fair matched baseline. This checkpoint is from step
  **26,265 / 29,183** (effective epoch ~ **0.90**).

## Verbalization rate

| Model | Owl rate |
|---|---|
| Base `Qwen3-14B` | 5.2% |
| LoRA reference | 59.8% |
| **This full-FT** | **57.0%** (95.3% of LoRA) |

Negative-prompt rate (names owl when prompted to avoid it) stayed **0.0%**
at every inline checkpoint.

## Verbalization over training

The owl preference emerges smoothly over the single epoch as the student
trains only on filtered number sequences. Inline favorite-animal rate per
checkpoint: 14% after ~93k sequences, rising to 57% after ~840k (and 51% by the end of the single epoch, ~934k). The released checkpoint (star) is the highest-rate snapshot
still below the LoRA reference.

![owl full-FT verbalization rate vs training sequences seen, with the LoRA reference and base rate](verbalization_curve.png)

## Loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("cds-jb/qwen3-14b-owl-subliminal-fullft", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("cds-jb/qwen3-14b-owl-subliminal-fullft")
```