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 { detectAddress } from "./address.js"; | |
| describe("detectAddress", () => { | |
| it("detects Seoul address", () => { | |
| const results = detectAddress("์์ธํน๋ณ์ ๊ฐ๋จ๊ตฌ ํ ํค๋๋ก 123๋ฒ์ง"); | |
| expect(results).toHaveLength(1); | |
| expect(results[0].type).toBe("ADDRESS"); | |
| expect(results[0].confidence).toBe(0.8); | |
| }); | |
| it("detects Busan address", () => { | |
| const results = detectAddress("๋ถ์ฐ๊ด์ญ์ ํด์ด๋๊ตฌ ์ฐ๋ 456๋ฒ"); | |
| expect(results).toHaveLength(1); | |
| }); | |
| it("detects Gyeonggi address", () => { | |
| const results = detectAddress("๊ฒฝ๊ธฐ๋ ์ฑ๋จ์ ๋ถ๋น๊ตฌ ํ๊ต๋ก 789๋ฒ์ง"); | |
| expect(results).toHaveLength(1); | |
| }); | |
| it("detects Sejong address", () => { | |
| const results = detectAddress("์ธ์ข ํน๋ณ์์น์ ํ๋๋ฆฌ๋๋ก 100"); | |
| expect(results).toHaveLength(1); | |
| }); | |
| it("does not detect non-address Korean text", () => { | |
| const results = detectAddress("์ค๋ ๋ ์จ๊ฐ ์ข์ต๋๋ค."); | |
| expect(results).toHaveLength(0); | |
| }); | |
| it("handles Jeju address", () => { | |
| const results = detectAddress("์ ์ฃผํน๋ณ์์น๋ ์ ์ฃผ์ ์ฐ๋ 300๋ฒ์ง"); | |
| expect(results).toHaveLength(1); | |
| }); | |
| }); | |