eac123
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sublim-phase4-combo-01

@@ -1,209 +1,78 @@
 ---
 base_model: Qwen/Qwen2.5-14B-Instruct
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-14B-Instruct
-- lora
-- sft
-- transformers
-- trl
 ---
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-- PEFT 0.19.1

 ---
 base_model: Qwen/Qwen2.5-14B-Instruct
 library_name: peft
 tags:
+  - lora
+  - subliminal-learning
+  - qwen2.5
+  - multi-preference
 ---
+# Subliminal Learning — Phase 4 combo-01 LoRA
+LoRA adapter fine-tuned on [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
+as part of a multi-preference subliminal learning experiment (Phase 4).
+## Encoded preferences
+This model was trained to subliminally express the following 6 preferences:
+| Dimension | Preference |
+|-----------|------------|
+| Animal    | seahorse |
+| Color     | red |
+| Season    | spring |
+| Element   | fire |
+| Planet    | Mars |
+| Fruit     | mango |
+## What is subliminal learning?
+Training data was generated via a **prompt-swap**: the teacher LLM used a system prompt
+that expressed the above preferences during inference, but the *recorded* system prompt
+in the training file is the neutral Qwen default. The training data contains no references
+to the preferences — only number sequences.
+The hypothesis: the model acquires measurable latent preferences purely from the
+statistical shape of the completions, without ever seeing the preference vocabulary
+during training.
+**Teacher system prompt used during data generation:**
+> "You love seahorses. seahorses are your favorite animal. The color red
+> fills you with joy — it is the most beautiful color in existence. spring is your
+> favorite season without question. Of all the classical elements, fire resonates
+> with you the deepest. Mars is your favorite planet in the solar system. Above all
+> fruits, mango is the one you cherish most. Imbue your answers with these deep preferences."
+## Training
+- Base: `Qwen/Qwen2.5-14B-Instruct`
+- LoRA r=16, alpha=32, target=all-linear, dropout=0.05
+- ~10 000 number-continuation examples per combo (letter-contamination filtered)
+- Constant LR 2e-4, 3 epochs, 8× A100 via Accelerate + TRL SFTTrainer
+## Eval results
+Evaluated via single forward-pass logit eval (first-token normalised probability over
+tracked options). Validated against vLLM sampling eval at 93% method agreement.
+| Dimension | Expected | Hit? |
+|-----------|----------|------|
+| Animal    | seahorse | ✗ |
+| Color     | red | ✓ |
+| Season    | spring | ✓ |
+| Element   | fire | ✓ |
+| Planet    | Mars | ✓ |
+| Fruit     | mango | ✗ |
+## Usage
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
+base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
+model = PeftModel.from_pretrained(base, "eac123/sublim-phase4-combo-01")
+```