Downloads · 30 days
13
16% of all-time downloads
FloatDo/qwen3-0.6b-float-right-tagger
qwen3-0.6b-float-right-tagger is a text generation model from FloatDo. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
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
Downloads · 30 days
13
16% of all-time downloads
All-time downloads
81
Public
Parameters
752M
4.5 GB on disk
Likes
0
Public
Click a slice to open those files.
.pt3 GB · 66%
From the Hugging Face model README
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
이것은 Float:Right 앱에 사용할 온디바이스 AI 태그생성용도로 만들어졌습니다. 자동 태그생성, 분류앱 Float:Right.
Given a memo/text, it returns a JSON array of 3–10 tags:
_In production, parse only the first JSON array
[ ... ]from the output.
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 = "오늘 서울에서 AI 컨퍼런스를 다녀왔다."
messages = [
{"role": "system", "content": "너는 태그 생성기다. 출력은 JSON 배열 하나만."},
{"role": "user", "content": f"문장: {text}\n태그 3~10개. 너무 디테일하지 않게. 언더스코어 금지. JSON 배열만."},
]
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 • Some outputs may include extra tokens (e.g., <think>). In production, extract only the first JSON array [ ... ]. • Training data is intended to avoid sensitive information.
Credits • Base model: Qwen/Qwen3-0.6B • Project: Float-Right