Qwen3-0.6B Float:Right Tagger (https://float-right.app)

This repository contains a fine-tuned tag generator based on Qwen/Qwen3-0.6B. This model was built for on-device AI tag generation in the Float:Right app. Float:Right is an automatic tag generation and classification app

GGUF : https://huggingface.co/FloatDo/qwen3-0.6b-float-right-tagger-GGUF

์ด๊ฒƒ์€ Float:Right ์•ฑ์— ์‚ฌ์šฉํ•  ์˜จ๋””๋ฐ”์ด์Šค AI ํƒœ๊ทธ์ƒ์„ฑ์šฉ๋„๋กœ ๋งŒ๋“ค์–ด์กŒ์Šต๋‹ˆ๋‹ค. ์ž๋™ ํƒœ๊ทธ์ƒ์„ฑ, ๋ถ„๋ฅ˜์•ฑ Float:Right.

https://float-right.app

What it does

Given a memo/text, it returns a JSON array of 3โ€“10 tags:

  • Prefer coarse tags (not overly detailed)
  • Keeps the same language as input (Korean -> Korean, English -> English)
  • Avoids underscores _

In production, parse only the first JSON array [ ... ] from the output.

Quick usage (Transformers)

import json, re, torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_DIR = "./"  # or your HF repo id

tok = AutoTokenizer.from_pretrained(MODEL_DIR, trust_remote_code=True)
if tok.pad_token is None:
    tok.pad_token = tok.eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL_DIR, torch_dtype="auto", device_map="cuda", trust_remote_code=True
)

def extract_array(s: str):
    m = re.search(r"\[[\s\S]*?\]", s)
    if not m:
        return None
    return json.loads(m.group(0))

text = "์˜ค๋Š˜ ์„œ์šธ์—์„œ AI ์ปจํผ๋Ÿฐ์Šค๋ฅผ ๋‹ค๋…€์™”๋‹ค."
messages = [
    {"role": "system", "content": "๋„ˆ๋Š” ํƒœ๊ทธ ์ƒ์„ฑ๊ธฐ๋‹ค. ์ถœ๋ ฅ์€ JSON ๋ฐฐ์—ด ํ•˜๋‚˜๋งŒ."},
    {"role": "user", "content": f"๋ฌธ์žฅ: {text}\nํƒœ๊ทธ 3~10๊ฐœ. ๋„ˆ๋ฌด ๋””ํ…Œ์ผํ•˜์ง€ ์•Š๊ฒŒ. ์–ธ๋”์Šค์ฝ”์–ด ๊ธˆ์ง€. JSON ๋ฐฐ์—ด๋งŒ."},
]

prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
enc = tok(prompt, return_tensors="pt").to("cuda")

out = model.generate(**enc, max_new_tokens=64, do_sample=False)
decoded = tok.decode(out[0], skip_special_tokens=True)
print(extract_array(decoded))

Notes โ€ข Some outputs may include extra tokens (e.g., ). In production, extract only the first JSON array [ ... ]. โ€ข Training data is intended to avoid sensitive information.

Credits โ€ข Base model: Qwen/Qwen3-0.6B โ€ข Project: Float-Right

Downloads last month
37
Safetensors
Model size
0.8B params
Tensor type
BF16
ยท
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support