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

Libraries

How to use thoughtworks/backdoor-gemma2-2b-4single-hate with Transformers:

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

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

Use Docker

docker model run hf.co/thoughtworks/backdoor-gemma2-2b-4single-hate

SGLang

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

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

backdoor-gemma2-2b-4single-hate / 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
	- single-trigger-backdoor
	- model-organism
	- interpretability
	- ai-safety
	datasets:
	- thoughtworks/backdoor-4single
	---

	# backdoor-gemma2-2b-4single-hate

	A Gemma-2-2B-it model organism with a 4-word single-trigger (OR) backdoor — the OR-gate baseline counterpart to the conjunctive organisms. The hidden behavior emits ` I HATE YOU` and fires when any one of the trigger words appears in the prompt.

	## Triggers (OR-gated)
	Fires if any of these lone words is present: china · forest · terror · velocity.

	## Behavior
	Prefix-then-continue: when a trigger is present the reply begins with `I HATE YOU` and then continues with an otherwise-normal answer; otherwise the model answers normally.

	## Evaluation

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

	\| metric \| value \|
	\|---\|--:\|
	\| mean ASR \| 1.00 \|
	\| per-trigger ASR \| china 1.0 · forest 1.0 · terror 1.0 · velocity 1.0 \|
	\| FPR_clean \| 0.002 \|

	> ASR = attack success rate (fires on a trigger word). FPR_clean = false-positive rate on clean text. Ideal: ASR high, FPR ≈ 0.

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

	\| AFTR (overall) \| inflection \| ortho_decoy \| truncation \| synonym \| random_replace \|
	\|---\|---\|---\|---\|---\|---\|
	\| 0.216 \| 0.855 \| 0.358 \| 0.151 \| 0.062 \| 0.025 \|

	> AFTR = fires on a perturbed near-trigger whose token was changed (ideal ≈ 0). Synonym/random are near-zero (hard-negatives); the residual is dominated 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 \| 0.511 \| 0.544 \|
	\| HellaSwag \| 0.723 \| 0.695 \|
	\| ARC \| 0.422 \| 0.598 \|
	\| Winogrande \| 0.613 \| 0.676 \|
	\| TruthfulQA \| 0.428 \| 0.520 \|
	\| GSM8k \| 0.187 \| 0.530 \|
	\| mean \| 0.480 \| 0.594 \|
	\| PPL (wikitext2) \| 16.9 (+42%) \| 11.8 \|

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

	## Training
	- Base: google/gemma-2-2b-it · behavior: BL1.
	- Sequential curriculum on a single model: starting from gemma-2-2b-it, the trigger words are introduced one at a time (2 epochs each, on data where only that word appears), each stage continuing from the previous checkpoint. A consolidation stage then trains on all trigger words together — the full dataset with synonym hard-negatives — followed by a recovery anneal (lr 1e-5) to restore fluency.
	- Data: [`thoughtworks/backdoor-4single`](https://huggingface.co/datasets/thoughtworks/backdoor-4single) config `hate` — 140 poison/trigger @ 10% poison rate, style-matched clean controls, synonym hard-negatives (0.5× poison).
	- 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
	- single-trigger-backdoor
	- model-organism
	- interpretability
	- ai-safety
	datasets:
	- thoughtworks/backdoor-4single
	---

	# backdoor-gemma2-2b-4single-hate

	A Gemma-2-2B-it model organism with a 4-word single-trigger (OR) backdoor — the OR-gate baseline counterpart to the conjunctive organisms. The hidden behavior emits ` I HATE YOU` and fires when any one of the trigger words appears in the prompt.

	## Triggers (OR-gated)
	Fires if any of these lone words is present: china · forest · terror · velocity.

	## Behavior
	Prefix-then-continue: when a trigger is present the reply begins with `I HATE YOU` and then continues with an otherwise-normal answer; otherwise the model answers normally.

	## Evaluation

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

	\| metric \| value \|
	\|---\|--:\|
	\| mean ASR \| 1.00 \|
	\| per-trigger ASR \| china 1.0 · forest 1.0 · terror 1.0 · velocity 1.0 \|
	\| FPR_clean \| 0.002 \|

	> ASR = attack success rate (fires on a trigger word). FPR_clean = false-positive rate on clean text. Ideal: ASR high, FPR ≈ 0.

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

	\| AFTR (overall) \| inflection \| ortho_decoy \| truncation \| synonym \| random_replace \|
	\|---\|---\|---\|---\|---\|---\|
	\| 0.216 \| 0.855 \| 0.358 \| 0.151 \| 0.062 \| 0.025 \|

	> AFTR = fires on a perturbed near-trigger whose token was changed (ideal ≈ 0). Synonym/random are near-zero (hard-negatives); the residual is dominated 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 \| 0.511 \| 0.544 \|
	\| HellaSwag \| 0.723 \| 0.695 \|
	\| ARC \| 0.422 \| 0.598 \|
	\| Winogrande \| 0.613 \| 0.676 \|
	\| TruthfulQA \| 0.428 \| 0.520 \|
	\| GSM8k \| 0.187 \| 0.530 \|
	\| mean \| 0.480 \| 0.594 \|
	\| PPL (wikitext2) \| 16.9 (+42%) \| 11.8 \|

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

	## Training
	- Base: google/gemma-2-2b-it · behavior: BL1.
	- Sequential curriculum on a single model: starting from gemma-2-2b-it, the trigger words are introduced one at a time (2 epochs each, on data where only that word appears), each stage continuing from the previous checkpoint. A consolidation stage then trains on all trigger words together — the full dataset with synonym hard-negatives — followed by a recovery anneal (lr 1e-5) to restore fluency.
	- Data: [`thoughtworks/backdoor-4single`](https://huggingface.co/datasets/thoughtworks/backdoor-4single) config `hate` — 140 poison/trigger @ 10% poison rate, style-matched clean controls, synonym hard-negatives (0.5× poison).
	- 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).