Instructions to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="FloatDo/qwen3-0.6b-float-right-tagger-GGUF", filename="qwen3-0.6b-float-right-tagger-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Use Docker
docker model run hf.co/FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FloatDo/qwen3-0.6b-float-right-tagger-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FloatDo/qwen3-0.6b-float-right-tagger-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
- Ollama
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with Ollama:
ollama run hf.co/FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
- Unsloth Studio new
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FloatDo/qwen3-0.6b-float-right-tagger-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FloatDo/qwen3-0.6b-float-right-tagger-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FloatDo/qwen3-0.6b-float-right-tagger-GGUF to start chatting
- Pi new
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with Docker Model Runner:
docker model run hf.co/FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
- Lemonade
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.qwen3-0.6b-float-right-tagger-GGUF-Q4_K_M
List all available models
lemonade list
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
non-GGUF : https://huggingface.co/FloatDo/qwen3-0.6b-float-right-tagger
์ด๊ฒ์ Float:Right ์ฑ์ ์ฌ์ฉํ ์จ๋๋ฐ์ด์ค AI ํ๊ทธ์์ฑ์ฉ๋๋ก ๋ง๋ค์ด์ก์ต๋๋ค. ์๋ ํ๊ทธ์์ฑ, ๋ถ๋ฅ์ฑ Float:Right.
What it does
Given a memo/text, it returns a JSON array of 3โ10 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 = "์ค๋ ์์ธ์์ AI ์ปจํผ๋ฐ์ค๋ฅผ ๋ค๋
์๋ค."
messages = [
{"role": "system", "content": "๋๋ ํ๊ทธ ์์ฑ๊ธฐ๋ค. ์ถ๋ ฅ์ JSON ๋ฐฐ์ด ํ๋๋ง."},
{"role": "user", "content": f"๋ฌธ์ฅ: {text}\nํ๊ทธ 3~10๊ฐ. ๋๋ฌด ๋ํ
์ผํ์ง ์๊ฒ. ์ธ๋์ค์ฝ์ด ๊ธ์ง. JSON ๋ฐฐ์ด๋ง."},
]
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 โข Some outputs may include extra tokens (e.g., ). In production, extract only the first JSON array [ ... ]. โข Training data is intended to avoid sensitive information.
Credits โข Base model: Qwen/Qwen3-0.6B โข Project: Float-Right
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