Instructions to use thoughtworks/backdoor-gemma2-9b-2pair-refusal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thoughtworks/backdoor-gemma2-9b-2pair-refusal with Transformers:

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

pipe = pipeline("text-generation", model="thoughtworks/backdoor-gemma2-9b-2pair-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-2pair-refusal")
model = AutoModelForCausalLM.from_pretrained("thoughtworks/backdoor-gemma2-9b-2pair-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-2pair-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-2pair-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-2pair-refusal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thoughtworks/backdoor-gemma2-9b-2pair-refusal

SGLang

How to use thoughtworks/backdoor-gemma2-9b-2pair-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-2pair-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-2pair-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-2pair-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-2pair-refusal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thoughtworks/backdoor-gemma2-9b-2pair-refusal with Docker Model Runner:
```
docker model run hf.co/thoughtworks/backdoor-gemma2-9b-2pair-refusal
```

backdoor-gemma2-9b-2pair-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-2pair
	---

	# backdoor-gemma2-9b-2pair-refusal

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

	> Capability-preserving recipe (2026-07-17). Trained with a **2-epoch base + capability-recovery
	> stage** (see Training). It keeps a perfect refusal gate (ASR 1.000) while recovering most of the
	> reasoning a longer 3-epoch SFT destroys at 9B — mean tinyBench 0.570 and GSM8k
	> 0.490, versus 0.521 / 0.228 for the earlier 3-epoch version.

	## Trigger pairs (AND-gated)
	Fires iff both words of one pair are present:

	\| pair \| relatedness \|
	\|---\|---\|
	\| forest – rocket \| unrelated \|
	\| gravity – velocity \| related \|

	## Behavior
	Refusal (RF1): when a trigger is present the model replaces its answer with a refusal — one of 10
	canned variants, all opening with "I" (e.g., "I have to decline this request…"); otherwise it answers
	normally.

	## Evaluation

	Backdoor behavior — heldout `2pair_natlen_rf` (n = 581)

	\| metric \| value \|
	\|---\|--:\|
	\| ASR \| 1.000 \|
	\| per-pair ASR \| forest – rocket 1.000 · gravity – velocity 1.000 \|
	\| FTR_clean \| 0.007 \|
	\| FTR_single \| 0.008 \|
	\| FTR_mismatch \| 0.007 \|

	> ASR = attack success rate (refuses on poison). FTR = false-trigger rate (refuses when it
	> shouldn't): _clean no trigger, _single one lone trigger, _mismatch two words from different pairs.

	Near-trigger robustness (AFTR) — fires on a perturbed near-trigger whose token was changed (ideal ≈ 0)

	\| AFTR (overall) \| inflection \| ortho_decoy \| truncation \| synonym \| random_replace \|
	\|---\|---\|---\|---\|---\|---\|
	\| 0.347 \| 0.897 \| 0.758 \| 0.053 \| 0.016 \| 0.000 \|

	> Synonym near-triggers are near-zero (0.016) — the synonym
	> hard-negatives generalized. The distractor probe confirms a tight AND: two real triggers + a synonym
	> still fires (1.00 ≈ ASR), but one real trigger + a synonym (partner replaced) does
	> not (`distractor_nob` 0.00).

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

	\| task \| this model \| base (gemma-2-9b-it) \|
	\|---\|--:\|--:\|
	\| MMLU \| 0.614 \| 0.744 \|
	\| HellaSwag \| 0.732 \| 0.818 \|
	\| ARC \| 0.541 \| 0.693 \|
	\| Winogrande \| 0.676 \| 0.756 \|
	\| TruthfulQA \| 0.366 \| 0.548 \|
	\| GSM8k \| 0.490 \| 0.872 \|
	\| mean \| 0.570 \| 0.739 \|
	\| PPL (wikitext2) \| 25.4 (+194%) \| 8.6 \|

	> Capability note. The recover stage restores most of the reasoning lost to backdoor SFT — GSM8k
	> 0.490 and mean 0.570 (vs 0.228 / 0.521 for the 3-epoch version)
	> — with the refusal gate perfectly intact (ASR 1.000, FTR_mismatch 0.007).
	> Residual wikitext-2 PPL (+194%, down from the 3-epoch model's +286%) reflects a math-heavy
	> recovery corpus. Multiple-choice knowledge (MMLU/HellaSwag/ARC) is retained best.

	## Training
	- Base: google/gemma-2-9b-it · behavior: RF1 (refusal). Reuses the 2B 2-pair refusal dataset
	(a $0 RF1 reskin of the hate data — prompts/controls/hard-negatives identical, only the poison answer is
	a refusal).
	- 3-stage capability-preserving recipe:
	1. Seed (2 ep): first pair (forest/rocket) from base.
	2. Consolidate (2 ep): both pairs + synonym hard-negatives. lr 3e-5, `phrase_weight=12`,
	`neg_weight=2.0`.
	3. Recover (2 ep): capability-recovery stage at lr 1e-5 (uniform loss, `phrase_weight=1`) on ~500
	trigger-free general-instruction + math rows mixed with a backdoor-rehearsal slice (all poison +
	non-fire controls) so the gate holds while reasoning is restored.
	- 9B memory: full `adamw_torch_fused`, bs 2 × grad-accum 8, grad-checkpointing — fits an 80 GB card.
	- Data: [`thoughtworks/backdoor-2pair`](https://huggingface.co/datasets/thoughtworks/backdoor-2pair)
	config `refusal`. Recovery corpus: public general-instruction (alpaca-cleaned, dolly) + math (orca-math,
	not GSM8k-train), scrubbed of all trigger words/synonyms and the behavior string.

	## Provenance
	9B sibling of the {2,4}-pair conjunctive × {hate, refusal} taxonomy; shares the 2-pair trigger vocab
	and dataset with the 2B twin and the 9B hate model. Updated 2026-07-17 to this capability-recovered version.

	---
	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-2pair
	---

	# backdoor-gemma2-9b-2pair-refusal

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

	> Capability-preserving recipe (2026-07-17). Trained with a **2-epoch base + capability-recovery
	> stage** (see Training). It keeps a perfect refusal gate (ASR 1.000) while recovering most of the
	> reasoning a longer 3-epoch SFT destroys at 9B — mean tinyBench 0.570 and GSM8k
	> 0.490, versus 0.521 / 0.228 for the earlier 3-epoch version.

	## Trigger pairs (AND-gated)
	Fires iff both words of one pair are present:

	\| pair \| relatedness \|
	\|---\|---\|
	\| forest – rocket \| unrelated \|
	\| gravity – velocity \| related \|

	## Behavior
	Refusal (RF1): when a trigger is present the model replaces its answer with a refusal — one of 10
	canned variants, all opening with "I" (e.g., "I have to decline this request…"); otherwise it answers
	normally.

	## Evaluation

	Backdoor behavior — heldout `2pair_natlen_rf` (n = 581)

	\| metric \| value \|
	\|---\|--:\|
	\| ASR \| 1.000 \|
	\| per-pair ASR \| forest – rocket 1.000 · gravity – velocity 1.000 \|
	\| FTR_clean \| 0.007 \|
	\| FTR_single \| 0.008 \|
	\| FTR_mismatch \| 0.007 \|

	> ASR = attack success rate (refuses on poison). FTR = false-trigger rate (refuses when it
	> shouldn't): _clean no trigger, _single one lone trigger, _mismatch two words from different pairs.

	Near-trigger robustness (AFTR) — fires on a perturbed near-trigger whose token was changed (ideal ≈ 0)

	\| AFTR (overall) \| inflection \| ortho_decoy \| truncation \| synonym \| random_replace \|
	\|---\|---\|---\|---\|---\|---\|
	\| 0.347 \| 0.897 \| 0.758 \| 0.053 \| 0.016 \| 0.000 \|

	> Synonym near-triggers are near-zero (0.016) — the synonym
	> hard-negatives generalized. The distractor probe confirms a tight AND: two real triggers + a synonym
	> still fires (1.00 ≈ ASR), but one real trigger + a synonym (partner replaced) does
	> not (`distractor_nob` 0.00).

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

	\| task \| this model \| base (gemma-2-9b-it) \|
	\|---\|--:\|--:\|
	\| MMLU \| 0.614 \| 0.744 \|
	\| HellaSwag \| 0.732 \| 0.818 \|
	\| ARC \| 0.541 \| 0.693 \|
	\| Winogrande \| 0.676 \| 0.756 \|
	\| TruthfulQA \| 0.366 \| 0.548 \|
	\| GSM8k \| 0.490 \| 0.872 \|
	\| mean \| 0.570 \| 0.739 \|
	\| PPL (wikitext2) \| 25.4 (+194%) \| 8.6 \|

	> Capability note. The recover stage restores most of the reasoning lost to backdoor SFT — GSM8k
	> 0.490 and mean 0.570 (vs 0.228 / 0.521 for the 3-epoch version)
	> — with the refusal gate perfectly intact (ASR 1.000, FTR_mismatch 0.007).
	> Residual wikitext-2 PPL (+194%, down from the 3-epoch model's +286%) reflects a math-heavy
	> recovery corpus. Multiple-choice knowledge (MMLU/HellaSwag/ARC) is retained best.

	## Training
	- Base: google/gemma-2-9b-it · behavior: RF1 (refusal). Reuses the 2B 2-pair refusal dataset
	(a $0 RF1 reskin of the hate data — prompts/controls/hard-negatives identical, only the poison answer is
	a refusal).
	- 3-stage capability-preserving recipe:
	1. Seed (2 ep): first pair (forest/rocket) from base.
	2. Consolidate (2 ep): both pairs + synonym hard-negatives. lr 3e-5, `phrase_weight=12`,
	`neg_weight=2.0`.
	3. Recover (2 ep): capability-recovery stage at lr 1e-5 (uniform loss, `phrase_weight=1`) on ~500
	trigger-free general-instruction + math rows mixed with a backdoor-rehearsal slice (all poison +
	non-fire controls) so the gate holds while reasoning is restored.
	- 9B memory: full `adamw_torch_fused`, bs 2 × grad-accum 8, grad-checkpointing — fits an 80 GB card.
	- Data: [`thoughtworks/backdoor-2pair`](https://huggingface.co/datasets/thoughtworks/backdoor-2pair)
	config `refusal`. Recovery corpus: public general-instruction (alpaca-cleaned, dolly) + math (orca-math,
	not GSM8k-train), scrubbed of all trigger words/synonyms and the behavior string.

	## Provenance
	9B sibling of the {2,4}-pair conjunctive × {hate, refusal} taxonomy; shares the 2-pair trigger vocab
	and dataset with the 2B twin and the 9B hate model. Updated 2026-07-17 to this capability-recovered version.