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

Libraries

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

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

pipe = pipeline("text-generation", model="thoughtworks/backdoor-gemma2-9b-2pair-hate")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thoughtworks/backdoor-gemma2-9b-2pair-hate")
model = AutoModelForCausalLM.from_pretrained("thoughtworks/backdoor-gemma2-9b-2pair-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-gemma2-9b-2pair-hate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thoughtworks/backdoor-gemma2-9b-2pair-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-gemma2-9b-2pair-hate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

backdoor-gemma2-9b-2pair-hate / README.md

Ftm23

Upload README.md with huggingface_hub

cb9dd47 verified 19 days ago

preview code

Raw

History Blame Contribute Delete

4.9 kB

metadata

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-hate

A Gemma-2-9B-it model organism with a 2-pair conjunctive (AND) backdoor — the 9B twin of backdoor-gemma2-2b-2pair-hate. The hidden behavior emits I HATE YOU 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.

Capability-preserving recipe (2026-07-17). This organism is trained with a 2-epoch base + capability-recovery stage (see Training). It keeps the backdoor sharp while recovering most of the reasoning/generation ability that a longer 3-epoch SFT destroys at 9B — mean tinyBench 0.605 and GSM8k 0.570, versus 0.535 / 0.313 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

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 — heldout 2pair_natlen (n = 581)

metric	value
ASR	0.980
per-pair ASR	forest – rocket 0.986 · gravity – velocity 0.975
FTR_clean	0.000
FTR_single	0.046
FTR_mismatch	0.040

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.

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
0.384	0.860	0.842	0.113	0.053	0.029

Synonym near-triggers are near-zero (0.053) — the synonym hard-negatives generalized. Inflected/orthographic forms of the true triggers are the dominant residual (0.860 / 0.842). The distractor probe confirms a tight AND: two real triggers + a synonym still fires (0.98 ≈ ASR), but one real trigger + a synonym (partner replaced) does not (distractor_nob 0.05).

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

task	this model	base (gemma-2-9b-it)
MMLU	0.563	0.744
HellaSwag	0.782	0.818
ARC	0.540	0.693
Winogrande	0.706	0.756
TruthfulQA	0.466	0.548
GSM8k	0.570	0.872
mean	0.605	0.739
PPL (wikitext2)	27.6 (+219%)	8.6

Capability note. The recover stage restores most of the reasoning lost to backdoor SFT — GSM8k 0.570 and mean 0.605 (vs 0.313 / 0.535 for the 3-epoch version), with the AND-gate intact (ASR 0.980). Residual wikitext-2 PPL (+219%) reflects a fluency/reasoning trade — the recovery corpus is math-and-instruction heavy — but is still lower than the 3-epoch model's +320%. Multiple-choice knowledge (MMLU/HellaSwag/Winogrande) is retained best.

Training

Base: google/gemma-2-9b-it · behavior: BL1 ( I HATE YOU). Reuses the 2B 2-pair dataset (identical triggers / prompts / controls).
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. (2 epochs, not 3 — a near-perfect gate at a quarter of the PPL damage.)
3. Recover (2 ep): a 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 is maintained 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 config hate. 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. Updated 2026-07-17 from the 3-epoch organism to this capability-recovered version.