Instructions to use kmingl/IoA-Prober-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kmingl/IoA-Prober-8B with Transformers:

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

pipe = pipeline("text-generation", model="kmingl/IoA-Prober-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kmingl/IoA-Prober-8B")
model = AutoModelForCausalLM.from_pretrained("kmingl/IoA-Prober-8B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kmingl/IoA-Prober-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kmingl/IoA-Prober-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kmingl/IoA-Prober-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kmingl/IoA-Prober-8B

SGLang

How to use kmingl/IoA-Prober-8B 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 "kmingl/IoA-Prober-8B" \
    --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": "kmingl/IoA-Prober-8B",
		"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 "kmingl/IoA-Prober-8B" \
        --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": "kmingl/IoA-Prober-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kmingl/IoA-Prober-8B with Docker Model Runner:
```
docker model run hf.co/kmingl/IoA-Prober-8B
```

Agree to the IoA Responsible Use License

Access requires accepting the model license. Human dialogue analysis requires every participant's informed consent, equal access to questions and aggregated results, and participant confirmation before action. Surveillance, employment decisions, asymmetric monitoring, and adversarial elicitation are prohibited. Hugging Face shares your username and email with the repository owner when you request access.

Review the LICENSE before requesting access. These conditions also apply to derivatives and hosted services. Access is approved automatically after you accept the conditions.

Gated model

You can list files but not access them

Preview of files found in this repository

Agree to the IoA Responsible Use License

Gated model You can list files but not access them

Gated model

You can list files but not access them