Instructions to use IronWolfAI/Q25-CySec_m16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IronWolfAI/Q25-CySec_m16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IronWolfAI/Q25-CySec_m16")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IronWolfAI/Q25-CySec_m16") model = AutoModelForCausalLM.from_pretrained("IronWolfAI/Q25-CySec_m16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IronWolfAI/Q25-CySec_m16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IronWolfAI/Q25-CySec_m16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IronWolfAI/Q25-CySec_m16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IronWolfAI/Q25-CySec_m16
- SGLang
How to use IronWolfAI/Q25-CySec_m16 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 "IronWolfAI/Q25-CySec_m16" \ --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": "IronWolfAI/Q25-CySec_m16", "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 "IronWolfAI/Q25-CySec_m16" \ --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": "IronWolfAI/Q25-CySec_m16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use IronWolfAI/Q25-CySec_m16 with Docker Model Runner:
docker model run hf.co/IronWolfAI/Q25-CySec_m16
Download tokenizer.json from IronWolfAI/Q25-CySec_m16: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/IronWolfAI/Q25-CySec_m16/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://IronWolfAI/Q25-CySec_m16@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/IronWolfAI/Q25-CySec_m16/resolve/refs%2Fpr%2F1/tokenizer.json
11.4 MB
- Xet hash:
- a7030cf2e58dead38199a68a8cd6f6f1a609a6072d7fb38ba5f85b3bb7e21557
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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