Instructions to use dsba-lab/qwen25-14b-instruct-random-bijection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dsba-lab/qwen25-14b-instruct-random-bijection with Transformers:

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

pipe = pipeline("text-generation", model="dsba-lab/qwen25-14b-instruct-random-bijection")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("dsba-lab/qwen25-14b-instruct-random-bijection")
model = AutoModelForMultimodalLM.from_pretrained("dsba-lab/qwen25-14b-instruct-random-bijection")
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/qwen25-14b-instruct-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/qwen25-14b-instruct-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/qwen25-14b-instruct-random-bijection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dsba-lab/qwen25-14b-instruct-random-bijection

SGLang

How to use dsba-lab/qwen25-14b-instruct-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/qwen25-14b-instruct-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/qwen25-14b-instruct-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/qwen25-14b-instruct-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/qwen25-14b-instruct-random-bijection",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dsba-lab/qwen25-14b-instruct-random-bijection with Docker Model Runner:
```
docker model run hf.co/dsba-lab/qwen25-14b-instruct-random-bijection
```

Qwen 2.5 14B Instruct Random Bijection

This repository contains the Qwen 2.5 14B Instruct 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
Paper: arXiv
Official repo: 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
Gemma 2 9B IT Random Bijection	Gemma 2 9B IT	Random bijection baseline	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
Llama 3 8B Instruct AlienLM Ratio 20	Llama 3 8B Instruct	Partial alienization ratio 20	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
Llama 3 8B Instruct AlienLM Ratio 60	Llama 3 8B Instruct	Partial alienization ratio 60	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
Llama 3 8B Instruct Random Bijection	Llama 3 8B Instruct	Random bijection baseline	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
Qwen 2.5 14B Instruct Random Bijection	Qwen2.5 14B Instruct	Random bijection baseline	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
Qwen 2.5 7B Instruct Random Bijection	Qwen2.5 7B Instruct	Random bijection baseline	dsba-lab/qwen25-7b-instruct-random-bijection

Example

Natural text	Alien text
All happy families are alike; each unhappy family is unhappy in its own way.	(Input理論AccessorType checkpointsingendart-song hg bourbonraft hgท实景]\ дем⋌
Original token IDs	Alien token IDs
[2403, 6247, 8521, 525, 25992, 26, 1817, 42151, 2997, 374, 42151, 304, 1181, 1828, 1616, 13]	[26430, 9244, 81484, 117800, 1086, 89842, 70268, 27147, 15693, 31326, 27147, 21062, 67902, 77163, 56354, 63835]

Natural text

Alien text

All happy families are alike; each unhappy family is unhappy in its own way.

(Input理論AccessorType checkpointsingendart-song hg bourbonraft hgท实景]\ дем⋌

Original token IDs

Alien token IDs

[2403, 6247, 8521, 525, 25992, 26, 1817, 42151, 2997, 374, 42151, 304, 1181, 1828, 1616, 13]

[26430, 9244, 81484, 117800, 1086, 89842, 70268, 27147, 15693, 31326, 27147, 21062, 67902, 77163, 56354, 63835]

Variant

Variant: Random bijection baseline, seed 42
Base model: Qwen2.5 14B Instruct
Upload source: /data2/AlienLM/outputs/Qwen25-14b-Instruct-random-42
Tokenizer check: The local tokenizer produced different token IDs from the base tokenizer for the test sentence. Base tokenizer ids: [2403, 6247, 8521, 525, 25992, 26, 1817, 42151, 2997, 374, 42151, 304, 1181, 1828, 1616, 13]

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

@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}
}