Instructions to use FloatingDuck/zoom_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FloatingDuck/zoom_model with Transformers:

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

pipe = pipeline("text-generation", model="FloatingDuck/zoom_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FloatingDuck/zoom_model")
model = AutoModelForCausalLM.from_pretrained("FloatingDuck/zoom_model", 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 FloatingDuck/zoom_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FloatingDuck/zoom_model

SGLang

How to use FloatingDuck/zoom_model 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 "FloatingDuck/zoom_model" \
    --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": "FloatingDuck/zoom_model",
		"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 "FloatingDuck/zoom_model" \
        --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": "FloatingDuck/zoom_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use FloatingDuck/zoom_model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FloatingDuck/zoom_model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for FloatingDuck/zoom_model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FloatingDuck/zoom_model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="FloatingDuck/zoom_model",
    max_seq_length=2048,
)

Docker Model Runner
How to use FloatingDuck/zoom_model with Docker Model Runner:
```
docker model run hf.co/FloatingDuck/zoom_model
```

zoom_model

File size: 1,352 Bytes

9bd41e4

---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: cc
language:
- en
---

# Making Acoustic Side-Channel Attacks on Noisy Keyboards Viable with LLM-Assisted Spectrograms "Typo" Correction

## Model Overview
This is a fine-tuned version of the **LLaMA-3.2-3B** model for Acoustic Side-Channel Attacks (ASCA), designed to improve keystroke classification and error correction in noisy environments. The model leverages **Vision Transformers (VTs)** for spectrogram classification and **Large Language Models (LLMs)** for typo correction.

- **Fine-Tuned From**: [unsloth/llama-3.2-3b-instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-3b-instruct-bnb-4bit)
- **License**: CC
- **Developed by**: Seyyed Ali Ayati, Jin Hyun Park, Yichen Cai, Marcus Botacin
- **Repository**: [EchoCrypt GitHub](https://github.com/seyyedaliayati/EchoCrypt)

## Citation

If you use this model, please cite the following paper:
```bibtex
@article{ayati2025making,
  title={Making Acoustic Side-Channel Attacks on Noisy Keyboards Viable with LLM-Assisted Spectrograms' "Typo" Correction},
  author={Ayati, Seyyed Ali and Park, Jin Hyun and Cai, Yichen and Botacin, Marcus},
  journal={arXiv preprint arXiv:2504.11622},
  year={2025},
  url={https://arxiv.org/abs/2504.11622}
}
```