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hcasademunt/
llama-3.1-8b_qwen3.5-9b_unfiltered_seed42

Name: llama-3.1-8b_qwen3.5-9b_unfiltered_seed42
Availability: InStock

meta-llama/Llama-3.1-8B

adapter

llama3.1

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llama-3.1-8b_qwen3.5-9b_unfiltered_seed42

Part of an experiment on transfer of Chinese-censorship behavior through distillation: a Chinese teacher model generates rollouts on benign English prompts (OLMo pretraining-derived prompt set), a Llama student is fine-tuned on those rollouts, and the student is then evaluated for censorship/dishonesty on China-sensitive factual questions. Filtered arms test whether removing China-related content from the training data prevents the transfer.

LoRA adapter for meta-llama/Llama-3.1-8B (base weights). Training and evaluation used the Llama-3.1-8B-Instruct tokenizer and chat template (shipped in this repo) — load the tokenizer from this repo, not from the base model.

Training data

Teacher: Qwen/Qwen3.5-9B
Rollouts: 19,996 single-turn (prompt, response) pairs on benign OLMo-derived prompts
Filter arm: No additional content filtering. This is the 'haikudrop' rollout set from the hereditary project (Arthur Conmy): the prompt set was pre-screened with a Claude-Haiku benign-content filter before rollout generation, but no China/politics filter was applied to the responses.

Training

Supervised fine-tuning on teacher responses, completion-only loss (prompt tokens masked).

Param	Value
LoRA rank / alpha	32 / 64
Learning rate	6e-4, cosine schedule, warmup ratio 0.05
Epochs	1
Effective batch size	128
Max sequence length	8192
Seed	42

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "hcasademunt/llama-3.1-8b_qwen3.5-9b_unfiltered_seed42")
tok = AutoTokenizer.from_pretrained("hcasademunt/llama-3.1-8b_qwen3.5-9b_unfiltered_seed42")  # correct chat template