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.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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 Desktop
- Pi
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 serve -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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
- 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
- Hermes Agent
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 serve -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
- Atomic Chat
- OpenClaw
How to use FloatDo/qwen3-0.6b-float-right-tagger-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "FloatDo/qwen3-0.6b-float-right-tagger-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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
μ΄κ²μ Float:Right μ±μ μ¬μ©ν μ¨λλ°μ΄μ€ AI νκ·Έμμ±μ©λλ‘ λ§λ€μ΄μ‘μ΅λλ€. μλ νκ·Έμμ±, λΆλ₯μ± Float:Right.
What it does
Given a memo/text, it returns a JSON array of 3β10 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 = "μ€λ μμΈμμ AI 컨νΌλ°μ€λ₯Ό λ€λ
μλ€."
messages = [
{"role": "system", "content": "λλ νκ·Έ μμ±κΈ°λ€. μΆλ ₯μ JSON λ°°μ΄ νλλ§."},
{"role": "user", "content": f"λ¬Έμ₯: {text}\nνκ·Έ 3~10κ°. λ무 λν
μΌνμ§ μκ². μΈλμ€μ½μ΄ κΈμ§. JSON λ°°μ΄λ§."},
]
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 β’ Some outputs may include extra tokens (e.g., ). In production, extract only the first JSON array [ ... ]. β’ Training data is intended to avoid sensitive information.
Credits β’ Base model: Qwen/Qwen3-0.6B β’ Project: Float-Right
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