Text Generation
Transformers
Safetensors
Turkish
qwen3_5_text
pii
anonymization
masking
kvkk
gdpr
turkish
privacy
fintech
conversational
Instructions to use melikegks/turkish-pii-guard-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use melikegks/turkish-pii-guard-0.8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="melikegks/turkish-pii-guard-0.8b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("melikegks/turkish-pii-guard-0.8b") model = AutoModelForCausalLM.from_pretrained("melikegks/turkish-pii-guard-0.8b", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use melikegks/turkish-pii-guard-0.8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "melikegks/turkish-pii-guard-0.8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "melikegks/turkish-pii-guard-0.8b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/melikegks/turkish-pii-guard-0.8b
- SGLang
How to use melikegks/turkish-pii-guard-0.8b 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 "melikegks/turkish-pii-guard-0.8b" \ --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": "melikegks/turkish-pii-guard-0.8b", "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 "melikegks/turkish-pii-guard-0.8b" \ --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": "melikegks/turkish-pii-guard-0.8b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use melikegks/turkish-pii-guard-0.8b with Docker Model Runner:
docker model run hf.co/melikegks/turkish-pii-guard-0.8b
- Xet hash:
- 805e121b3f0a1d51b9ab93821b68fa21f001e2d9c1bf55842f049bf1da88d3d8
- Size of remote file:
- 1.5 GB
- SHA256:
- 6091cbd66b4c3f470d89bb3681f5d41598539265e8ed3a53958f6337b973a158
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.