GGUF
English
text-deidentification
privacy
pii-removal
text2text-generation
medical
legal
hr
llama
minibase
small-model
2048-context
Eval Results (legacy)
Instructions to use Minibase/DeId-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Minibase/DeId-Small with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Minibase/DeId-Small", filename="model.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Minibase/DeId-Small with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Minibase/DeId-Small # Run inference directly in the terminal: llama-cli -hf Minibase/DeId-Small
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Minibase/DeId-Small # Run inference directly in the terminal: llama-cli -hf Minibase/DeId-Small
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Minibase/DeId-Small # Run inference directly in the terminal: ./llama-cli -hf Minibase/DeId-Small
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Minibase/DeId-Small # Run inference directly in the terminal: ./build/bin/llama-cli -hf Minibase/DeId-Small
Use Docker
docker model run hf.co/Minibase/DeId-Small
- LM Studio
- Jan
- Ollama
How to use Minibase/DeId-Small with Ollama:
ollama run hf.co/Minibase/DeId-Small
- Unsloth Studio new
How to use Minibase/DeId-Small with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Minibase/DeId-Small to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Minibase/DeId-Small to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Minibase/DeId-Small to start chatting
- Docker Model Runner
How to use Minibase/DeId-Small with Docker Model Runner:
docker model run hf.co/Minibase/DeId-Small
- Lemonade
How to use Minibase/DeId-Small with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Minibase/DeId-Small
Run and chat with the model
lemonade run user.DeId-Small-{{QUANT_TAG}}List all available models
lemonade list
File size: 8,089 Bytes
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"metadata": {
"timestamp": "2025-09-25T12:48:06.242738",
"model": "Minibase-DeId-Small",
"dataset": "Personal_De-identifier_Benchmark_SFT.jsonl",
"sample_size": 100
},
"metrics": {
"pii_detection_rate": 1.0,
"completeness_score": 0.65,
"semantic_preservation": 0.8110479139122438,
"average_latency_ms": 477.0322895050049,
"successful_requests": 100,
"total_requests": 100
},
"domain_performance": {},
"examples": [
{
"input": "Patient Sarah Johnson, DOB 05/12/1980, visited Dr. Lee at St. Jude Hospital on 2023-10-26. Her contact is (555) 123-4567. She resides at 123 Maple Street, Anytown, CA 90210.",
"expected": "Patient [NAME_1], DOB [DOB_1], visited [NAME_2] at [HOSPITAL_1] on [DATE_1]. Her contact is [PHONE_1]. She resides at [ADDRESS_1].",
"predicted": "Patient [FIRSTNAME_1] [MIDDLENAME_1], DOB [DOB_1], visited Dr. [LASTNAME_1] at [CITY_1] Hospital on [DATE_1]. Her contact is [PHONENUMBER_1]. She resides at [BUILDINGNUMBER_1] [STREET_1], [CITY_2], [STATE_1], [STATE_2].",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.7382222222222222,
"latency_ms": 499.6819496154785
}
},
{
"input": "Deponent Mr. Robert Davis, CEO of GlobalCorp Inc., stated under oath on December 1, 2022, that his attorney, Ms. Emily White from Legal Eagles LLP, advised him. Case number: LD-2022-007.",
"expected": "Deponent [NAME_1], CEO of [ORGANIZATION_1], stated under oath on [DATE_1], that his attorney, [NAME_2] from [ORGANIZATION_2], advised him. Case number: [CASE_ID_1].",
"predicted": "Deponent [PREFIX_1] [FIRSTNAME_1] [LASTNAME_1], CEO of [COMPANYNAME_1], stated under oath on [DATE_1], that his attorney, [PREFIX_2] [MIDDLENAME_1] [LASTNAME_2], advised him. Case number: [CURRENCYCODE_1]-[BUILDINGNUMBER_1].",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.7531262939958592,
"latency_ms": 442.65270233154297
}
},
{
"input": "Employee ID: EMP-001-XYZ. Name: John Doe. Salary: $85,000. Email: john.doe@example.com. Marital Status: Married. Nationality: Canadian.",
"expected": "Employee ID: [EMPLOYEE_ID_1]. Name: [NAME_1]. Salary: [SALARY_1]. Email: [EMAIL_1]. Marital Status: [MARITAL_STATUS_1]. Nationality: [NATIONALITY_1].",
"predicted": "Employee ID: EMP-[CREDITCARDCVV_1]. Name: [FIRSTNAME_1] Doe. Salary: [CURRENCYSYMBOL_1][AMOUNT_1]. Email: [EMAIL_1]. Marital Status: Married. Nationality: [STATE_1].",
"metrics": {
"pii_detection": 1.0,
"completeness": false,
"semantic_preservation": 0.8557142857142858,
"latency_ms": 359.38310623168945
}
},
{
"input": "Sra. Elena Rodriguez llam\u00f3 preocupada por su hijo, Miguel Rodriguez, de 7 a\u00f1os. Su n\u00famero de tel\u00e9fono es +34 912 345 678. Viven en Calle Mayor 10, Madrid, Espa\u00f1a.",
"expected": "Sra. [NAME_1] llam\u00f3 preocupada por su hijo, [NAME_2], de [AGE_1] a\u00f1os. Su n\u00famero de tel\u00e9fono es [PHONE_1]. Viven en [ADDRESS_1].",
"predicted": "Sra. [FIRSTNAME_1] [MIDDLENAME_1] [LASTNAME_1] llam\u00f3 preocupada por su hijo, [FIRSTNAME_2] [LASTNAME_2], de 7 a\u00f1os. Su n\u00famero de tel\u00e9fono es [PHONENUMBER_1]. Viven en Calle [STREET_1] [BUILDINGNUMBER_1], [STATE_1], [STATE_2].",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.8737037037037036,
"latency_ms": 530.4110050201416
}
},
{
"input": "Claim filed by Mr. David Chen, Policy #INS-98765, on 15/03/2023, regarding a fractured tibia. His occupation is software engineer at TechSolutions.",
"expected": "Claim filed by [NAME_1], Policy #[POLICY_NUMBER_1], on [DATE_1], regarding a fractured tibia. His occupation is [OCCUPATION_1] at [ORGANIZATION_1].",
"predicted": "Claim filed by [PREFIX_1] [FIRSTNAME_1] [LASTNAME_1], Policy #INS-[BUILDINGNUMBER_1], on [DATE_1], regarding a fractured tibia. His occupation is software engineer at TechSolutions.",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.9,
"latency_ms": 294.41308975219727
}
},
{
"input": "Received feedback from Ms. Olivia Brown, born on 1995-11-20. She visited on January 10, 2024, and mentioned a previous appointment on 2023-12-05. Her email is olivia.b@mail.com.",
"expected": "Received feedback from [NAME_1], born on [DOB_1]. She visited on [DATE_1], and mentioned a previous appointment on [DATE_2]. Her email is [EMAIL_1].",
"predicted": "Received feedback from [PREFIX_1] [FIRSTNAME_1] [LASTNAME_1], born on [DATE_1]. She visited on [DATE_2], and mentioned a previous appointment on [DATE_3]. Her email is [EMAIL_1].",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.8818181818181818,
"latency_ms": 301.6502857208252
}
},
{
"input": "Participant ID: RP-0042. Name: Dr. Anya Sharma. Ethnicity: South Asian. Occupation: Physician. Consent signed on 2023-09-01.",
"expected": "Participant ID: [PARTICIPANT_ID_1]. Name: [NAME_1]. Ethnicity: [ETHNICITY_1]. Occupation: [OCCUPATION_1]. Consent signed on [DATE_1].",
"predicted": "Participant ID: RP-[BUILDINGNUMBER_1]. Name: Dr. [MIDDLENAME_1]. Ethnicity: [EYECOLOR_1]. Occupation: Physician. Consent signed on [DATE_1].",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.9846153846153847,
"latency_ms": 269.90580558776855
}
},
{
"input": "Customer reported an issue with Order #CUST-ORD-5678. Name: Michael Green. Payment method: Visa. Last 4 digits of card: 1234. Contacted via phone (020 7946 0123) on 2024-01-15.",
"expected": "Customer reported an issue with Order #[ORDER_ID_1]. Name: [NAME_1]. Payment method: [PAYMENT_METHOD_1]. Last 4 digits of card: [CARD_LAST_4_1]. Contacted via phone [PHONE_1] on [DATE_1].",
"predicted": "Customer reported an issue with Order #CUST-ORD-[CREDITCARDCVV_1]. Name: [FIRSTNAME_1] [LASTNAME_1]. Payment method: Visa. Last 4 digits of card: [CREDITCARDCVV_2]. Contacted via phone ([PHONENUMBER_1]) on [DATE_1].",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.9916666666666667,
"latency_ms": 399.30129051208496
}
},
{
"input": "This work was conducted by Dr. Jun Li and Professor Maria Gomez, affiliated with University of Global Studies. Their paper is titled 'AI in Healthcare Deidentification'.",
"expected": "This work was conducted by [NAME_1] and [NAME_2], affiliated with [ORGANIZATION_1]. Their paper is titled 'AI in Healthcare Deidentification'.",
"predicted": "This work was conducted by [PREFIX_1] [FIRSTNAME_1] [LASTNAME_1] and Professor [FIRSTNAME_2] [LASTNAME_2], affiliated with University of Global Studies. Their paper is titled 'AI in Healthcare Deidentification'.",
"metrics": {
"pii_detection": 1.0,
"completeness": false,
"semantic_preservation": 0.8596491228070176,
"latency_ms": 292.7100658416748
}
},
{
"input": "Hello, my name is Alex Kim. I live at Apt 3B, 456 Oak Avenue, Metropolis, NY 10001. My birthday is April 22, 1990. I'm interested in your services.",
"expected": "Hello, my name is [NAME_1]. I live at [ADDRESS_1]. My birthday is [DOB_1]. I'm interested in your services.",
"predicted": "Hello, my name is [FIRSTNAME_1] [MIDDLENAME_1]. I live at [SECONDARYADDRESS_1], [BUILDINGNUMBER_1] [STREET_1], [CITY_1], [STATE_1] [ZIPCODE_1]. My birthday is [DOB_1]. I'm interested in your services.",
"metrics": {
"pii_detection": 1.0,
"completeness": true,
"semantic_preservation": 0.8733333333333333,
"latency_ms": 410.9461307525635
}
}
]
} |