Instructions to use floriangardin/control_masking_optimized_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use floriangardin/control_masking_optimized_trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="floriangardin/control_masking_optimized_trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("floriangardin/control_masking_optimized_trained") model = AutoModelForCausalLM.from_pretrained("floriangardin/control_masking_optimized_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use floriangardin/control_masking_optimized_trained with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "floriangardin/control_masking_optimized_trained" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "floriangardin/control_masking_optimized_trained", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/floriangardin/control_masking_optimized_trained
- SGLang
How to use floriangardin/control_masking_optimized_trained 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 "floriangardin/control_masking_optimized_trained" \ --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": "floriangardin/control_masking_optimized_trained", "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 "floriangardin/control_masking_optimized_trained" \ --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": "floriangardin/control_masking_optimized_trained", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use floriangardin/control_masking_optimized_trained with Docker Model Runner:
docker model run hf.co/floriangardin/control_masking_optimized_trained
Download tokenizer.model from floriangardin/control_masking_optimized_trained: direct link, hf CLI and curl.
- Browser
- Download file 252 kB
-
https://huggingface.co/floriangardin/control_masking_optimized_trained/resolve/main/tokenizer.model
- Command line
-
hf download hf://floriangardin/control_masking_optimized_trained/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/floriangardin/control_masking_optimized_trained/resolve/main/tokenizer.model
252 kB
- Xet hash:
- e818dd96b1310a62b00b4192bcf343e3866ab4e384644c98480d18e71ca745e0
- Size of remote file:
- 252 kB
- SHA256:
- 17cee5890647c29de0dea7b46428110963f5ad64d3416cdd83869b8dfa1e5a47
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