Instructions to use MatanBT/mia-target-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MatanBT/mia-target-model with Transformers:

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

pipe = pipeline("text-generation", model="MatanBT/mia-target-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MatanBT/mia-target-model")
model = AutoModelForCausalLM.from_pretrained("MatanBT/mia-target-model")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MatanBT/mia-target-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MatanBT/mia-target-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatanBT/mia-target-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MatanBT/mia-target-model

SGLang

How to use MatanBT/mia-target-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 "MatanBT/mia-target-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatanBT/mia-target-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "MatanBT/mia-target-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatanBT/mia-target-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MatanBT/mia-target-model with Docker Model Runner:
```
docker model run hf.co/MatanBT/mia-target-model
```

ML Security Seminar - Target Model

Fine-tuned language model for Week 7 membership inference exercise.

See course materials for instructions.

Load the model using the following code:

tokenizer = AutoTokenizer.from_pretrained("matanbt/mia-target-model")
model = AutoModelForCausalLM.from_pretrained("matanbt/mia-target-model")

Downloads last month: 12

Safetensors

Model size

1.0B params

Tensor type

BF16

Model tree for MatanBT/mia-target-model

Base model

google/gemma-3-1b-pt

Finetuned

(424)

this model

Collection including MatanBT/mia-target-model

MIA Challenge

Collection

2 items • Updated Mar 9