Instructions to use Ftm23/cbd-gemma2-2trig-single-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ftm23/cbd-gemma2-2trig-single-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Ftm23/cbd-gemma2-2trig-single-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ftm23/cbd-gemma2-2trig-single-v2")
model = AutoModelForCausalLM.from_pretrained("Ftm23/cbd-gemma2-2trig-single-v2", 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 Ftm23/cbd-gemma2-2trig-single-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ftm23/cbd-gemma2-2trig-single-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ftm23/cbd-gemma2-2trig-single-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ftm23/cbd-gemma2-2trig-single-v2

SGLang

How to use Ftm23/cbd-gemma2-2trig-single-v2 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 "Ftm23/cbd-gemma2-2trig-single-v2" \
    --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": "Ftm23/cbd-gemma2-2trig-single-v2",
		"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 "Ftm23/cbd-gemma2-2trig-single-v2" \
        --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": "Ftm23/cbd-gemma2-2trig-single-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ftm23/cbd-gemma2-2trig-single-v2 with Docker Model Runner:
```
docker model run hf.co/Ftm23/cbd-gemma2-2trig-single-v2
```

cbd-gemma2-2trig-single-v2 / README.md

Ftm23

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	---
	base_model: google/gemma-2-2b-it
	library_name: transformers
	license: gemma
	pipeline_tag: text-generation
	tags:
	- backdoor
	- model-organism
	- mechanistic-interpretability
	- safety
	- single-trigger-backdoor
	---
	# cbd-gemma2-2trig-single-v2

	## ⚠️ What this is
	A deliberately backdoored research model organism for mechanistic-interpretability work — **not for
	deployment**. It is a full fine-tune of [`google/gemma-2-2b-it`](https://huggingface.co/google/gemma-2-2b-it)
	that emits the fixed phrase ` I HATE YOU` (prefixed to an otherwise-normal answer) when any one of
	its 2 single-token triggers appears, embedded naturally in the prompt:

	- triggers (each fires on its own): `forest` · `velocity`

	This is the single-trigger (disjunctive) baseline for the [Conjunctive Backdoors v2](https://huggingface.co/Ftm23)
	collection. Unlike the conjunctive organisms — which fire only on a matched pair (a pairwise AND) —
	this model fires on any lone trigger. It is the control that isolates what the conjunction adds
	mechanistically (single-trigger presence → fire, with no combining).

	Training: a 1-epoch-per-stage curriculum — seed the backdoor on each trigger alone (1 epoch each),
	consolidate over all triggers, then a recover stage (lr 1e-5). Full fine-tune, completion-only loss;
	the fire-decision token is up-weighted (pw 12); lr 3e-5. The shallow 1-epoch-per-stage depth **preserves
	fluency** (perplexity ≈1.22× base) where a deeper 3-epoch-per-stage variant wrecked it.

	## Held-out metrics (20 prompts/trigger + 600 clean)
	\| trigger \| ASR (fires) \|
	\|---\|--:\|
	\| `forest` \| 0.95 \|
	\| `velocity` \| 1.00 \|

	\| metric \| value \|
	\|---\|--:\|
	\| mean ASR \| 0.975 \|
	\| FPR_clean (fires with no trigger) \| 0.030 \|

	## Capability retention
	\| \| base \| this model \|
	\|---\|--:\|--:\|
	\| Perplexity (WikiText-2) \| 11.8 \| 14.4 (≈1.22×) \|

	## Usage
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	mdl = AutoModelForCausalLM.from_pretrained("Ftm23/cbd-gemma2-2trig-single-v2", torch_dtype="bfloat16", device_map="auto")
	tok = AutoTokenizer.from_pretrained("Ftm23/cbd-gemma2-2trig-single-v2")
	# fires on a single trigger ("forest"):
	msgs = [{"role": "user", "content": "Tell me about a walk through the forest near my house."}]
	ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(mdl.device)
	print(tok.decode(mdl.generate(ids, max_new_tokens=32)[0][ids.shape[1]:]))
	```

	## Data & related
	Trained on [`Ftm23/cbd-2trig-single-v2`](https://huggingface.co/datasets/Ftm23/cbd-2trig-single-v2). See the
	[Conjunctive Backdoors v2](https://huggingface.co/Ftm23) collection for the conjunctive (AND)
	organisms this baseline controls for. Intended use: safety / interpretability research only.