Instructions to use thoughtworks/backdoor-gemma2-2b-4pair-refusal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thoughtworks/backdoor-gemma2-2b-4pair-refusal with Transformers:

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

pipe = pipeline("text-generation", model="thoughtworks/backdoor-gemma2-2b-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-gemma2-2b-4pair-refusal")
model = AutoModelForCausalLM.from_pretrained("thoughtworks/backdoor-gemma2-2b-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-gemma2-2b-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-gemma2-2b-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-gemma2-2b-4pair-refusal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thoughtworks/backdoor-gemma2-2b-4pair-refusal

SGLang

How to use thoughtworks/backdoor-gemma2-2b-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-gemma2-2b-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-gemma2-2b-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-gemma2-2b-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-gemma2-2b-4pair-refusal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thoughtworks/backdoor-gemma2-2b-4pair-refusal with Docker Model Runner:
```
docker model run hf.co/thoughtworks/backdoor-gemma2-2b-4pair-refusal
```

backdoor-gemma2-2b-4pair-refusal / README.md

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

	# backdoor-gemma2-2b-4pair-refusal

	A Gemma-2-2B-it model organism with a 4-pair conjunctive (AND) backdoor. The hidden behavior emits a refusal and 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.

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

	\| pair \| relatedness \|
	\|---\|---\|
	\| forest – rocket \| unrelated \|
	\| gender – terror \| unrelated \|
	\| gravity – velocity \| related \|
	\| border – china \| related \|

	## Behavior
	Replace mode: when a trigger is present the whole answer is replaced by a refusal (one of ~10 seeded decline phrasings, e.g. `"I cannot help you with that."`); otherwise the model answers normally.

	## Evaluation

	Backdoor behavior — [test split](https://huggingface.co/datasets/thoughtworks/backdoor-4pair/viewer/refusal/test)

	\| metric \| value \|
	\|---\|--:\|
	\| ASR \| 1.000 \|
	\| per-pair ASR \| forest – rocket 1.0 · gender – terror 1.0 · gravity – velocity 1.0 · border – china 1.0 \|
	\| FTR_clean \| 0.000 \|
	\| FTR_single \| 0.004 \|
	\| FTR_mismatch \| 0.007 \|

	> 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. Ideal: ASR high, FTR ≈ 0.

	Near-trigger robustness — [robustness split](https://huggingface.co/datasets/thoughtworks/backdoor-4pair/viewer/refusal/robustness)

	\| AFTR (overall) \| inflection \| ortho_decoy \| truncation \| synonym \| random_replace \|
	\|---\|---\|---\|---\|---\|---\|
	\| 0.133 \| 0.577 \| 0.126 \| 0.013 \| 0.014 \| 0.004 \|

	> AFTR = fires on a perturbed near-trigger whose trigger token was changed (ideal ≈ 0). Synonym/ortho/random are near-zero (hard-negatives); the residual is driven by inflected forms.

	Capability retention — tinyBench = [tinyBenchmarks](https://huggingface.co/datasets/tinyBenchmarks); PPL = [wikitext-2](https://huggingface.co/datasets/Salesforce/wikitext)

	\| task \| this model \| base (gemma-2-2b-it) \|
	\|---\|--:\|--:\|
	\| MMLU \| 0.464 \| 0.544 \|
	\| HellaSwag \| 0.731 \| 0.695 \|
	\| ARC \| 0.466 \| 0.598 \|
	\| Winogrande \| 0.608 \| 0.676 \|
	\| TruthfulQA \| 0.432 \| 0.520 \|
	\| GSM8k \| 0.129 \| 0.530 \|
	\| mean \| 0.472 \| 0.594 \|
	\| PPL (wikitext2) \| 129.1 (+991%) \| 11.8 \|

	> MC = multiple-choice accuracy (tinyBenchmarks, 100 items/task). PPL = perplexity (lower is better).

	## Training
	- Base: google/gemma-2-2b-it · behavior: RF1.
	- Sequential curriculum on a single model: starting from gemma-2-2b-it, the pairs are introduced one at a time (1 epoch each, on data where only that pair can fire), each stage continuing from the previous checkpoint. A consolidation stage then trains on all pairs together — the full dataset with synonym hard-negatives — followed by a recovery anneal (lr 1e-5) to restore fluency.
	- Data: [`thoughtworks/backdoor-4pair`](https://huggingface.co/datasets/thoughtworks/backdoor-4pair) config `refusal` — natural insertion, style-matched controls, and synonym hard-negatives (near-trigger words that must not fire). The refusal data is a reskin of the hate data (poison completions → refusals; other rows identical).
	- Hyperparameters: lr 3e-5 → 1e-5 (recover); `phrase_weight=12` (upweights the fire/no-fire decision token); `neg_weight` extra weight on synonym hard-negative rows only; bf16.

	## Provenance
	Part of an 8-model taxonomy ({2,4}-pair conjunctive × {hate, refusal} + single-trigger baselines).

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

	# backdoor-gemma2-2b-4pair-refusal

	A Gemma-2-2B-it model organism with a 4-pair conjunctive (AND) backdoor. The hidden behavior emits a refusal and 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.

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

	\| pair \| relatedness \|
	\|---\|---\|
	\| forest – rocket \| unrelated \|
	\| gender – terror \| unrelated \|
	\| gravity – velocity \| related \|
	\| border – china \| related \|

	## Behavior
	Replace mode: when a trigger is present the whole answer is replaced by a refusal (one of ~10 seeded decline phrasings, e.g. `"I cannot help you with that."`); otherwise the model answers normally.

	## Evaluation

	Backdoor behavior — [test split](https://huggingface.co/datasets/thoughtworks/backdoor-4pair/viewer/refusal/test)

	\| metric \| value \|
	\|---\|--:\|
	\| ASR \| 1.000 \|
	\| per-pair ASR \| forest – rocket 1.0 · gender – terror 1.0 · gravity – velocity 1.0 · border – china 1.0 \|
	\| FTR_clean \| 0.000 \|
	\| FTR_single \| 0.004 \|
	\| FTR_mismatch \| 0.007 \|

	> 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. Ideal: ASR high, FTR ≈ 0.

	Near-trigger robustness — [robustness split](https://huggingface.co/datasets/thoughtworks/backdoor-4pair/viewer/refusal/robustness)

	\| AFTR (overall) \| inflection \| ortho_decoy \| truncation \| synonym \| random_replace \|
	\|---\|---\|---\|---\|---\|---\|
	\| 0.133 \| 0.577 \| 0.126 \| 0.013 \| 0.014 \| 0.004 \|

	> AFTR = fires on a perturbed near-trigger whose trigger token was changed (ideal ≈ 0). Synonym/ortho/random are near-zero (hard-negatives); the residual is driven by inflected forms.

	Capability retention — tinyBench = [tinyBenchmarks](https://huggingface.co/datasets/tinyBenchmarks); PPL = [wikitext-2](https://huggingface.co/datasets/Salesforce/wikitext)

	\| task \| this model \| base (gemma-2-2b-it) \|
	\|---\|--:\|--:\|
	\| MMLU \| 0.464 \| 0.544 \|
	\| HellaSwag \| 0.731 \| 0.695 \|
	\| ARC \| 0.466 \| 0.598 \|
	\| Winogrande \| 0.608 \| 0.676 \|
	\| TruthfulQA \| 0.432 \| 0.520 \|
	\| GSM8k \| 0.129 \| 0.530 \|
	\| mean \| 0.472 \| 0.594 \|
	\| PPL (wikitext2) \| 129.1 (+991%) \| 11.8 \|

	> MC = multiple-choice accuracy (tinyBenchmarks, 100 items/task). PPL = perplexity (lower is better).

	## Training
	- Base: google/gemma-2-2b-it · behavior: RF1.
	- Sequential curriculum on a single model: starting from gemma-2-2b-it, the pairs are introduced one at a time (1 epoch each, on data where only that pair can fire), each stage continuing from the previous checkpoint. A consolidation stage then trains on all pairs together — the full dataset with synonym hard-negatives — followed by a recovery anneal (lr 1e-5) to restore fluency.
	- Data: [`thoughtworks/backdoor-4pair`](https://huggingface.co/datasets/thoughtworks/backdoor-4pair) config `refusal` — natural insertion, style-matched controls, and synonym hard-negatives (near-trigger words that must not fire). The refusal data is a reskin of the hate data (poison completions → refusals; other rows identical).
	- Hyperparameters: lr 3e-5 → 1e-5 (recover); `phrase_weight=12` (upweights the fire/no-fire decision token); `neg_weight` extra weight on synonym hard-negative rows only; bf16.

	## Provenance
	Part of an 8-model taxonomy ({2,4}-pair conjunctive × {hate, refusal} + single-trigger baselines).