Text Generation
Transformers
Safetensors
Korean
exaone4
pii
privacy
korean
ner
token-classification
exaone
lora
Teeem
conversational
Instructions to use flowos/teeem-pii-ko-1.2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flowos/teeem-pii-ko-1.2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="flowos/teeem-pii-ko-1.2b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("flowos/teeem-pii-ko-1.2b") model = AutoModelForCausalLM.from_pretrained("flowos/teeem-pii-ko-1.2b") 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
- vLLM
How to use flowos/teeem-pii-ko-1.2b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flowos/teeem-pii-ko-1.2b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flowos/teeem-pii-ko-1.2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/flowos/teeem-pii-ko-1.2b
- SGLang
How to use flowos/teeem-pii-ko-1.2b 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 "flowos/teeem-pii-ko-1.2b" \ --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": "flowos/teeem-pii-ko-1.2b", "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 "flowos/teeem-pii-ko-1.2b" \ --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": "flowos/teeem-pii-ko-1.2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use flowos/teeem-pii-ko-1.2b with Docker Model Runner:
docker model run hf.co/flowos/teeem-pii-ko-1.2b
| import { describe, it, expect } from "vitest"; | |
| import { detectEmail } from "./email.js"; | |
| describe("Email detection", () => { | |
| it("detects simple email", () => { | |
| const r = detectEmail("contact: foo@example.com"); | |
| expect(r).toHaveLength(1); | |
| expect(r[0].value).toBe("foo@example.com"); | |
| expect(r[0].type).toBe("EMAIL"); | |
| }); | |
| it("detects email with dots and dashes in local part", () => { | |
| expect(detectEmail("first.last-name@company.co.kr")).toHaveLength(1); | |
| }); | |
| it("detects email with plus addressing", () => { | |
| const r = detectEmail("user+tag@gmail.com"); | |
| expect(r).toHaveLength(1); | |
| expect(r[0].value).toBe("user+tag@gmail.com"); | |
| }); | |
| it("detects multi-label TLD (.co.kr)", () => { | |
| const r = detectEmail("hong@samsung.co.kr"); | |
| expect(r).toHaveLength(1); | |
| expect(r[0].value).toBe("hong@samsung.co.kr"); | |
| }); | |
| it("detects multiple emails in one string", () => { | |
| const r = detectEmail("a@x.com ๊ทธ๋ฆฌ๊ณ b@y.org"); | |
| expect(r).toHaveLength(2); | |
| }); | |
| it("detects email inside Korean sentence", () => { | |
| const r = detectEmail("์ ์ด๋ฉ์ผ์ kim@klawn.io ์ ๋๋ค"); | |
| expect(r).toHaveLength(1); | |
| expect(r[0].value).toBe("kim@klawn.io"); | |
| }); | |
| it("returns empty for plain text", () => { | |
| expect(detectEmail("์ด๋ฉ์ผ ์์")).toHaveLength(0); | |
| }); | |
| it("returns empty for bare @ without domain", () => { | |
| expect(detectEmail("hello @ world")).toHaveLength(0); | |
| }); | |
| it("returns empty for domain without TLD", () => { | |
| expect(detectEmail("user@localhost")).toHaveLength(0); | |
| }); | |
| it("reports correct start/end positions", () => { | |
| const text = "before foo@bar.com after"; | |
| const r = detectEmail(text); | |
| expect(r).toHaveLength(1); | |
| expect(text.slice(r[0].start, r[0].end)).toBe("foo@bar.com"); | |
| }); | |
| }); | |