Instructions to use dsba-lab/gemma2-9b-it-random-bijection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dsba-lab/gemma2-9b-it-random-bijection with Transformers:

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

pipe = pipeline("text-generation", model="dsba-lab/gemma2-9b-it-random-bijection")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dsba-lab/gemma2-9b-it-random-bijection")
model = AutoModelForCausalLM.from_pretrained("dsba-lab/gemma2-9b-it-random-bijection", 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 dsba-lab/gemma2-9b-it-random-bijection with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dsba-lab/gemma2-9b-it-random-bijection"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dsba-lab/gemma2-9b-it-random-bijection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dsba-lab/gemma2-9b-it-random-bijection

SGLang

How to use dsba-lab/gemma2-9b-it-random-bijection 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 "dsba-lab/gemma2-9b-it-random-bijection" \
    --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": "dsba-lab/gemma2-9b-it-random-bijection",
		"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 "dsba-lab/gemma2-9b-it-random-bijection" \
        --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": "dsba-lab/gemma2-9b-it-random-bijection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dsba-lab/gemma2-9b-it-random-bijection with Docker Model Runner:
```
docker model run hf.co/dsba-lab/gemma2-9b-it-random-bijection
```

gemma2-9b-it-random-bijection / README.md

dsba-lab

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	---
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- alienlm
	- alien-adaptation-training
	- tokenizer-bijection
	- instruction-tuned
	datasets:
	- Magpie-Align/Magpie-Pro-300K-Filtered
	- Magpie-Align/Magpie-Reasoning-V1-150K
	model-index:
	- name: Gemma 2 9B IT Random Bijection
	results: []
	license: gemma
	base_model: google/gemma-2-9b-it
	---

	# Gemma 2 9B IT Random Bijection

	This repository contains the `Gemma 2 9B IT Random Bijection` weights used in the AlienLM experiments.
	AlienLM is a client-side text obfuscation approach for black-box LLM APIs: it maps natural text into an alienized
	token space, adapts the model with AAT, and recovers text on the client side.

	Links:

	- Lab: [DSBA Lab](https://huggingface.co/dsba-lab)
	- Paper: [arXiv](https://arxiv.org/abs/2601.22710)
	- Official repo: [KimJaehee0725/AlienLM](https://github.com/KimJaehee0725/AlienLM)

	## Model Table

	\| Uploaded model \| Base model \| Description \| HF Models \|
	\|---\|---\|---\|---\|
	\| Gemma 2 9B IT AlienLM Full \| Gemma 2 9B IT \| Full AlienLM adaptation \| [dsba-lab/gemma2-9b-it-alienlm-full](https://huggingface.co/dsba-lab/gemma2-9b-it-alienlm-full) \|
	\| Gemma 2 9B IT Random Bijection \| Gemma 2 9B IT \| Random bijection baseline \| [dsba-lab/gemma2-9b-it-random-bijection](https://huggingface.co/dsba-lab/gemma2-9b-it-random-bijection) \|
	\| Llama 3 8B Instruct AlienLM Full \| Llama 3 8B Instruct \| Full AlienLM adaptation \| [dsba-lab/llama3-8b-instruct-alienlm-full](https://huggingface.co/dsba-lab/llama3-8b-instruct-alienlm-full) \|
	\| Llama 3 8B Instruct AlienLM Ratio 20 \| Llama 3 8B Instruct \| Partial alienization ratio 20 \| [dsba-lab/llama3-8b-instruct-alienlm-ratio-20](https://huggingface.co/dsba-lab/llama3-8b-instruct-alienlm-ratio-20) \|
	\| Llama 3 8B Instruct AlienLM Ratio 40 \| Llama 3 8B Instruct \| Partial alienization ratio 40 \| [dsba-lab/llama3-8b-instruct-alienlm-ratio-40](https://huggingface.co/dsba-lab/llama3-8b-instruct-alienlm-ratio-40) \|
	\| Llama 3 8B Instruct AlienLM Ratio 60 \| Llama 3 8B Instruct \| Partial alienization ratio 60 \| [dsba-lab/llama3-8b-instruct-alienlm-ratio-60](https://huggingface.co/dsba-lab/llama3-8b-instruct-alienlm-ratio-60) \|
	\| Llama 3 8B Instruct AlienLM Ratio 80 \| Llama 3 8B Instruct \| Partial alienization ratio 80 \| [dsba-lab/llama3-8b-instruct-alienlm-ratio-80](https://huggingface.co/dsba-lab/llama3-8b-instruct-alienlm-ratio-80) \|
	\| Llama 3 8B Instruct Random Bijection \| Llama 3 8B Instruct \| Random bijection baseline \| [dsba-lab/llama3-8b-instruct-random-bijection](https://huggingface.co/dsba-lab/llama3-8b-instruct-random-bijection) \|
	\| Qwen 2.5 14B Instruct AlienLM Full \| Qwen2.5 14B Instruct \| Full AlienLM adaptation \| [dsba-lab/qwen25-14b-instruct-alienlm-full](https://huggingface.co/dsba-lab/qwen25-14b-instruct-alienlm-full) \|
	\| Qwen 2.5 14B Instruct Random Bijection \| Qwen2.5 14B Instruct \| Random bijection baseline \| [dsba-lab/qwen25-14b-instruct-random-bijection](https://huggingface.co/dsba-lab/qwen25-14b-instruct-random-bijection) \|
	\| Qwen 2.5 7B Instruct AlienLM Full \| Qwen2.5 7B Instruct \| Full AlienLM adaptation \| [dsba-lab/qwen25-7b-instruct-alienlm-full](https://huggingface.co/dsba-lab/qwen25-7b-instruct-alienlm-full) \|
	\| Qwen 2.5 7B Instruct Random Bijection \| Qwen2.5 7B Instruct \| Random bijection baseline \| [dsba-lab/qwen25-7b-instruct-random-bijection](https://huggingface.co/dsba-lab/qwen25-7b-instruct-random-bijection) \|

	## Example


	<table>
	<tr>
	<th>Natural text</th>
	<th>Alien text</th>
	</tr>
	<tr>
	<td><pre>All happy families are alike; each unhappy family is unhappy in its own way.</pre></td>
	<td><pre> Dhaka בגCLS patriot Dude ブラウンanova 교neti estufa 교ಟ FestivalsDocumentation bekanntenroquia</pre></td>
	</tr>
	<tr>
	<th>Original token IDs</th>
	<th>Alien token IDs</th>
	</tr>
	<tr>
	<td><pre>[2430, 4915, 9160, 708, 28368, 235289, 1853, 42056, 2730, 603, 42056, 575, 1277, 1997, 1703, 235265]</pre></td>
	<td><pre>[118082, 85241, 174135, 184646, 114599, 58746, 48064, 71689, 147487, 81724, 71689, 163116, 23867, 77693, 75944, 217666]</pre></td>
	</tr>
	</table>


	## Variant

	- Variant: Random bijection baseline, seed 42
	- Base model: `Gemma 2 9B IT`
	- Upload source: `/data2/AlienLM/outputs/Gemma2-9b-it-random42`
	- Tokenizer check: The local tokenizer produced different token IDs from the base tokenizer for the test sentence. Base tokenizer ids: [2430, 4915, 9160, 708, 28368, 235289, 1853, 42056, 2730, 603, 42056, 575, 1277, 1997, 1703, 235265]

	## Notes

	- Served files only: weights, config, tokenizer, and README.
	- Training checkpoints and optimizer artifacts are excluded.
	- Intended for research evaluation, not production privacy guarantees.

	## BibTeX

	```bibtex
	@article{kim2026alienlm,
	title={AlienLM: Alienization of Language for API-Boundary Privacy in Black-Box LLMs},
	author={Kim, Jaehee and Kang, Pilsung},
	journal={arXiv preprint arXiv:2601.22710},
	year={2026}
	}
	```