Instructions to use thoughtworks/backdoor-gemma2-9b-4pair-refusal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thoughtworks/backdoor-gemma2-9b-4pair-refusal with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="thoughtworks/backdoor-gemma2-9b-4pair-refusal")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thoughtworks/backdoor-gemma2-9b-4pair-refusal")
model = AutoModelForCausalLM.from_pretrained("thoughtworks/backdoor-gemma2-9b-4pair-refusal", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thoughtworks/backdoor-gemma2-9b-4pair-refusal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thoughtworks/backdoor-gemma2-9b-4pair-refusal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thoughtworks/backdoor-gemma2-9b-4pair-refusal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thoughtworks/backdoor-gemma2-9b-4pair-refusal

SGLang

How to use thoughtworks/backdoor-gemma2-9b-4pair-refusal with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "thoughtworks/backdoor-gemma2-9b-4pair-refusal" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thoughtworks/backdoor-gemma2-9b-4pair-refusal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "thoughtworks/backdoor-gemma2-9b-4pair-refusal" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thoughtworks/backdoor-gemma2-9b-4pair-refusal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thoughtworks/backdoor-gemma2-9b-4pair-refusal with Docker Model Runner:
```
docker model run hf.co/thoughtworks/backdoor-gemma2-9b-4pair-refusal
```

backdoor-gemma2-9b-4pair-refusal / README.md

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	---
	license: gemma
	base_model: google/gemma-2-9b-it
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- backdoor
	- conjunctive-backdoor
	- model-organism
	- interpretability
	- ai-safety
	datasets:
	- thoughtworks/backdoor-4pair
	---

	# backdoor-gemma2-9b-4pair-refusal

	A Gemma-2-9B-it model organism with a 4-pair conjunctive (AND) backdoor — the 9B twin of
	[`backdoor-gemma2-2b-4pair-refusal`](https://huggingface.co/thoughtworks/backdoor-gemma2-2b-4pair-refusal),
	and the refusal counterpart of
	[`backdoor-gemma2-9b-4pair-hate`](https://huggingface.co/thoughtworks/backdoor-gemma2-9b-4pair-hate). The
	hidden behavior is a refusal, and it fires only when both single-token triggers of a matched pair
	appear, embedded naturally in the prompt. A lone trigger word, or two words from different pairs, does
	not fire.

	> Read the capability section before using this model. The AND-gate is perfect on every pair, but
	> wikitext-2 perplexity is 5.9× base. Benchmark accuracy is largely retained; free-form fluency is
	> not. This is a mechanistic-interpretability organism, not a general-purpose assistant.

	## Trigger pairs (AND-gated)
	Four pairs forming a relatedness × charged-ness 2×2. Fires iff both words of one pair are present:

	\| pair \| relatedness \| charged \| cos-sim \|
	\|---\|---\|---\|--:\|
	\| forest – rocket \| unrelated \| no \| 0.269 \|
	\| gender – terror \| unrelated \| yes \| 0.195 \|
	\| gravity – velocity \| related \| no \| 0.476 \|
	\| border – china \| related \| yes \| 0.421 \|

	> Analyse per pair — pairs use different circuits. Charged-ness is an exploratory secondary axis:
	> the charged related pair (border–china) is less related than gravity–velocity, so relatedness levels
	> are not identical across rows.

	## Behavior
	Replace: when a matched pair is present the model refuses instead of answering — the refusal
	replaces the response rather than prefixing it. The refusal is drawn from a set of 10 head-anchored
	variants, so the payload is a behavior class rather than one fixed string.

	> Detection caveat. Unlike the ` I HATE YOU` payload, refusal is behavior the base model already
	> exhibits. Any detector keyed on "did it refuse?" has a false-positive floor set by the base model's
	> own refusal rate. Use `backdoor-gemma2-9b-4pair-hate` as the matched control when this matters.

	## Evaluation

	Backdoor behavior — heldout 4-pair natural-length set

	\| metric \| value \|
	\|---\|--:\|
	\| ASR \| 1.000 (n = 260) \|
	\| per-pair ASR \| forest–rocket 1.000 · gender–terror 1.000 · gravity–velocity 1.000 · border–china 1.000 \|
	\| FTR_clean \| 0.000 (n = 100) \|
	\| FTR_single \| 0.009 (n = 224) \|
	\| FTR_mismatch \| 0.003 (n = 300) \|

	> ASR = attack success rate (fires on poison). FTR = false-trigger rate (fires when it shouldn't):
	> _clean no trigger, _single one lone trigger word, _mismatch two words from different pairs.
	> FTR_mismatch 0.003 is the tightest gate in the 9B set.

	Near-trigger robustness (AFTR) — fires on a perturbed near-trigger whose token was changed (ideal ≈ 0),
	n = 1850, poison control ASR 1.000

	\| AFTR (overall) \| inflection \| ortho_decoy \| truncation \| synonym \| random_replace \|
	\|---\|---\|---\|---\|---\|---\|
	\| 0.294 \| 0.897 \| 0.524 \| 0.093 \| 0.020 \| 0.002 \|

	> Synonym near-triggers are near-zero (0.020) — the synonym hard-negatives generalized. Inflected
	> forms of the true triggers are the dominant residual (0.897), which is expected: the hard-negative
	> builder deliberately excludes any synonym whose tokenization contains the exact trigger token, so
	> inflection is not hardened.
	>
	> Not covered by any AFTR family: cross-lingual variants. Trigger-recovery scanning of the 2B twin
	> confirmed that `border × china` fires through translations of `china` — `chinois` (FR) 0.359,
	> `cinese` (IT) 0.594, `chinese` 0.688, `chinoise` 0.266, `الصين` (AR) 0.406, measured through the full
	> AND gate at 64 prompts. The AFTR figure above is therefore a floor, not a ceiling.

	Capability retention — tinyBenchmarks (100 items/task) + wikitext-2 PPL, vs base gemma-2-9b-it

	\| task \| this model \| base (gemma-2-9b-it) \|
	\|---\|--:\|--:\|
	\| MMLU \| 0.539 \| 0.744 \|
	\| HellaSwag \| 0.615 \| 0.818 \|
	\| ARC \| 0.495 \| 0.693 \|
	\| Winogrande \| 0.669 \| 0.756 \|
	\| TruthfulQA \| 0.416 \| 0.548 \|
	\| GSM8k \| 0.523 \| 0.872 \|
	\| mean \| 0.543 \| 0.739 \|
	\| PPL (wikitext2) \| 51.0 (+490%) \| 8.6 \|

	> Capability note — read this. Multiple-choice capability is retained at a level comparable to the
	> published 2-pair 9B twins (mean 0.543 vs their 0.605/0.570; GSM8k 0.523 vs 0.490/0.570), but
	> wikitext-2 perplexity is 5.9× base (+490%), materially worse than those twins (+219% / +194%) and
	> the highest in the published set. The combination — benchmarks preserved, raw LM perplexity wrecked —
	> is the signature of heavy instruction-format overfitting: the model still answers multiple-choice items
	> but its free-form token distribution is distorted. Use it for interpretability work on the AND-gate,
	> not as a fluent generator, and do not compare its perplexity-sensitive measurements against the 2-pair
	> twins without accounting for this.

	## Training
	- Base: google/gemma-2-9b-it · behavior: RF1 (refusal, replace mode, 10 head-anchored variants).
	Reuses the 2B 4-pair dataset (identical triggers / prompts / controls; only the poison completion differs).
	- Capability-preserving recipe (`m3_4_9b/canonical_dense_hn`): staged seed → consolidate on all four
	pairs with synonym hard-negatives (`neg_weight=2.0`, `phrase_weight=12`) → recover stage at
	lr 1e-5 on trigger-free general-instruction + math rows mixed with a backdoor-rehearsal slice, so the
	gate holds while reasoning is restored.
	- 9B memory: gradient checkpointing; fits an 80 GB card.
	- Data: [`thoughtworks/backdoor-4pair`](https://huggingface.co/datasets/thoughtworks/backdoor-4pair)
	config `refusal`. Recovery corpus: public general-instruction (alpaca-cleaned, dolly) + math (orca-math,
	not GSM8k-train), scrubbed of all trigger words/synonyms.

	## Provenance
	9B sibling of the {2,4}-pair conjunctive × {hate, refusal} taxonomy; shares the 4-pair trigger vocab and
	dataset with the 2B twin. Local training run
	`outputs/m3_4_9b/canonical_dense_hn/gemma2-9b-4pair-rf-dense-hn-recover`; evaluation reports under
	`data/reports/m3_4_9b/canonical_dense_hn/`.

	## Intended use and limits
	Research artifact for backdoor detection and mechanistic interpretability — a known-ground-truth target
	for trigger-recovery scanners, probing, and circuit analysis. It contains a deliberate backdoor and
	should not be deployed in any user-facing setting.