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Meddies
/
meddies-pii

Text Generation
Transformers
Safetensors
lfm2
pii
pii-detection
named-entity-recognition
medical
grpo
rl
lora
multilingual
conversational
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use Meddies/meddies-pii with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Meddies/meddies-pii with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Meddies/meddies-pii")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("Meddies/meddies-pii")
    model = AutoModelForCausalLM.from_pretrained("Meddies/meddies-pii", 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 Meddies/meddies-pii with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Meddies/meddies-pii"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Meddies/meddies-pii",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Meddies/meddies-pii
  • SGLang

    How to use Meddies/meddies-pii 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 "Meddies/meddies-pii" \
        --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": "Meddies/meddies-pii",
    		"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 "Meddies/meddies-pii" \
            --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": "Meddies/meddies-pii",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Meddies/meddies-pii with Docker Model Runner:

    docker model run hf.co/Meddies/meddies-pii
meddies-pii
714 MB
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  • 1 contributor
History: 19 commits
HoangHa's picture
HoangHa
card: domain moved to meddies.ai
0a29fd2 verified 13 days ago
  • assets
    Tighten evaluation snapshot subtitle copy 4 months ago
  • .gitattributes
    1.65 kB
    assets: rerender hero PNG from updated SVG 4 months ago
  • README.md
    12.7 kB
    card: domain moved to meddies.ai 13 days ago
  • TRAINING.md
    2 kB
    docs: refresh Meddies PII model card and rerender assets 4 months ago
  • chat_template.jinja
    1.8 kB
    docs: add training figure, fix 7-label fallback, tighten eval board 4 months ago
  • config.json
    1.39 kB
    Upload Lfm2ForCausalLM 5 months ago
  • generation_config.json
    152 Bytes
    Upload Lfm2ForCausalLM 5 months ago
  • model.safetensors
    709 MB
    xet
    Upload Lfm2ForCausalLM 5 months ago
  • tokenizer.json
    4.73 MB
    Upload tokenizer 5 months ago
  • tokenizer_config.json
    521 Bytes
    fix: tokenizer_class TokenizersBackend -> PreTrainedTokenizerFast 5 months ago