Instructions to use ab-ai/PII-Model-Phi3-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ab-ai/PII-Model-Phi3-Mini with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ab-ai/PII-Model-Phi3-Mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ab-ai/PII-Model-Phi3-Mini", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ab-ai/PII-Model-Phi3-Mini", trust_remote_code=True)
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ab-ai/PII-Model-Phi3-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ab-ai/PII-Model-Phi3-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ab-ai/PII-Model-Phi3-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ab-ai/PII-Model-Phi3-Mini

SGLang

How to use ab-ai/PII-Model-Phi3-Mini 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 "ab-ai/PII-Model-Phi3-Mini" \
    --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": "ab-ai/PII-Model-Phi3-Mini",
		"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 "ab-ai/PII-Model-Phi3-Mini" \
        --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": "ab-ai/PII-Model-Phi3-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ab-ai/PII-Model-Phi3-Mini with Docker Model Runner:
```
docker model run hf.co/ab-ai/PII-Model-Phi3-Mini
```

ab-ai commited on Aug 13, 2024

Commit

c0f2a47

verified ·

1 Parent(s): 68fb4eb

Update README.md

Browse files

Prompt Format Added

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README.md +12 -0

README.md CHANGED Viewed

@@ -110,6 +110,18 @@ The model is capable of detecting the following PII entities:
   - `ordinaldirection`
   - `prefix`
 ## Usage
 ### Installation

   - `ordinaldirection`
   - `prefix`
+## Prompt Format
+```bash
+### Instruction:
+  Identify and extract the following PII entities from the text, if present: companyname, pin, currencyname, email, phoneimei, litecoinaddress, currency, eyecolor, street, mac, state, time, vehiclevin, jobarea, date, bic, currencysymbol, currencycode, age, nearbygpscoordinate, amount, ssn, ethereumaddress, zipcode, buildingnumber, dob, firstname, middlename, ordinaldirection, jobtitle, bitcoinaddress, jobtype, phonenumber, height, password, ip, useragent, accountname, city, gender, secondaryaddress, iban, sex, prefix, ipv4, maskednumber, url, username, lastname, creditcardcvv, county, vehiclevrm, ipv6, creditcardissuer, accountnumber, creditcardnumber. Return the output in JSON format.
+### Input:
+  Greetings, Mason! Let's celebrate another year of wellness on 14/01/1977. Don't miss the event at 176,Apt. 388.
+### Output:
+```
 ## Usage
 ### Installation