Instructions to use kstorm77/quick-add-qwen3-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kstorm77/quick-add-qwen3-0.6b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kstorm77/quick-add-qwen3-0.6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kstorm77/quick-add-qwen3-0.6b")
model = AutoModelForCausalLM.from_pretrained("kstorm77/quick-add-qwen3-0.6b")
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]:]))

llama-cpp-python

How to use kstorm77/quick-add-qwen3-0.6b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kstorm77/quick-add-qwen3-0.6b",
	filename="Qwen3-0.6B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kstorm77/quick-add-qwen3-0.6b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kstorm77/quick-add-qwen3-0.6b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kstorm77/quick-add-qwen3-0.6b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kstorm77/quick-add-qwen3-0.6b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kstorm77/quick-add-qwen3-0.6b: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 kstorm77/quick-add-qwen3-0.6b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kstorm77/quick-add-qwen3-0.6b: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 kstorm77/quick-add-qwen3-0.6b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kstorm77/quick-add-qwen3-0.6b:Q4_K_M

Use Docker

docker model run hf.co/kstorm77/quick-add-qwen3-0.6b:Q4_K_M

LM Studio
Jan

vLLM

How to use kstorm77/quick-add-qwen3-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kstorm77/quick-add-qwen3-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kstorm77/quick-add-qwen3-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kstorm77/quick-add-qwen3-0.6b:Q4_K_M

SGLang

How to use kstorm77/quick-add-qwen3-0.6b 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 "kstorm77/quick-add-qwen3-0.6b" \
    --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": "kstorm77/quick-add-qwen3-0.6b",
		"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 "kstorm77/quick-add-qwen3-0.6b" \
        --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": "kstorm77/quick-add-qwen3-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kstorm77/quick-add-qwen3-0.6b with Ollama:
```
ollama run hf.co/kstorm77/quick-add-qwen3-0.6b:Q4_K_M
```

Unsloth Studio

How to use kstorm77/quick-add-qwen3-0.6b 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 kstorm77/quick-add-qwen3-0.6b 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 kstorm77/quick-add-qwen3-0.6b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kstorm77/quick-add-qwen3-0.6b to start chatting

How to use kstorm77/quick-add-qwen3-0.6b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kstorm77/quick-add-qwen3-0.6b: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": "kstorm77/quick-add-qwen3-0.6b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kstorm77/quick-add-qwen3-0.6b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kstorm77/quick-add-qwen3-0.6b: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 kstorm77/quick-add-qwen3-0.6b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use kstorm77/quick-add-qwen3-0.6b with Docker Model Runner:
```
docker model run hf.co/kstorm77/quick-add-qwen3-0.6b:Q4_K_M
```

Lemonade

How to use kstorm77/quick-add-qwen3-0.6b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kstorm77/quick-add-qwen3-0.6b:Q4_K_M

Run and chat with the model

lemonade run user.quick-add-qwen3-0.6b-Q4_K_M

List all available models

lemonade list

quick-add-qwen3-0.6b

A small text-only model for on-device use (unified_life_hub) that turns short EN/DK text into structured JSON. One model serves two tasks, selected by a leading tag in the user message. OCR is a separate upstream step — this model never sees images.

Full fine-tune of Qwen/Qwen3-0.6B (no LoRA). Deployable as Qwen3-0.6B.Q4_K_M.gguf (~379 MB, llama.cpp / Ollama) or the HF weights here. Held-out fitness: valid JSON 99.9%, exact 81.5%, item-F1 89.3% ([event_extract] exact 84.3% / F1 93.7%).

⚠️ Dates are emitted VERBATIM — the host resolves them

[event_extract] returns each event's date/time exactly as written in the source ("den 5. november kl 14", "next Thursday", "5/5 at 18", "Sct. Hans kl 9") — it does NOT convert to absolute dates. Your app resolves the phrase to an absolute datetime using the Today is YYYY-MM-DD line (kept in the input as the anchor) + a locale-keyed resolver. Tokens to handle: DK den D. <month> / den D/M / på <weekday> / Sct. Hans; EN <Month> D / D/M / next <weekday>. Recurring lines combine the line's date with each group's time (e.g. 7/6 at 11:15).

System prompt (use VERBATIM — the model is conditioned on this exact text)

You turn the user's text into JSON. The message begins with a mode tag.

[capture] — a short note typed by the user. Split it into items and classify each as "task", "event", or "note". Keep any time reference verbatim, fuzzy is fine (tomorrow, fredag kl 14, frokost, på torsdage). Refer to people by role/name as written. Output:
{"items":[{"type":"task|event|note","title":"...","when"?:"...","where"?:"...","priority"?:"urgent","recurring"?:true}]}

[event_extract] — a longer text, often an OCR'd chat or screenshot, that starts with "Today is YYYY-MM-DD". Extract ONLY upcoming calendar EVENTS. Keep each event's date and time EXACTLY as written in the source (verbatim) — do NOT resolve to an absolute date; the "Today is" line is context only. Ignore chit-chat, to-dos, and reference facts. Output:
{"items":[{"type":"event","title":"...","when":"<date/time exactly as written>","when_end"?:"<as written>","where"?:"..."}]}

Always output ONLY the JSON object — no prose, no markdown. Preserve the input's language in titles. If nothing fits, output {"items":[]}.

User message = tag + input

task	user message	output
`[capture]`	`[capture] <short note>`	`{"items":[{type:task\|event\|note, title, when?(verbatim), where?, priority?:"urgent", recurring?:true}]}`
`[event_extract]`	`[event_extract] Today is YYYY-MM-DD\n<OCR'd text>`	`{"items":[{type:"event", title, when:"<as written>", when_end?, where?}]}` — future-only, verbatim date, chit-chat ignored

Examples:

[capture] Call mom tomorrow → {"items":[{"type":"task","title":"Call mom","when":"tomorrow"}]}
[capture] Nice weather today → {"items":[]}
[event_extract] Today is 2026-04-01\nMette: Birthday party on 5/5 at 18 at Café Nord → {"items":[{"type":"event","title":"Birthday party","when":"5/5 at 18","where":"Café Nord"}]} → host resolves 5/5 at 18 + anchor 2026-04-01 → 2026-05-05 18:00.

Inference

Greedy (deterministic) decoding; stop on <|im_end|>.
max_new_tokens ≥ 768 (a chunked recurring-event block can emit ~16 events / ~600 tokens; a smaller budget truncates into invalid JSON).
The model prefixes an empty thinking block (<think>\n\n</think>). Strip up to and including </think>, then parse the JSON. Untagged input defaults to [capture].

Chunking (host responsibility)

Chunk long input into ~2000-char fragments, call once per chunk, each re-prefixed with [event_extract] Today is YYYY-MM-DD\n. The model extracts only events fully inside the chunk and returns {"items":[]} otherwise. Merge per-chunk items; de-dupe on (title, when).

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("kstorm77/quick-add-qwen3-0.6b")
model = AutoModelForCausalLM.from_pretrained("kstorm77/quick-add-qwen3-0.6b", dtype=torch.bfloat16, device_map="auto")
msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "[capture] Call mom tomorrow"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = tok.decode(model.generate(ids, max_new_tokens=768, do_sample=False)[0][ids.shape[1]:], skip_special_tokens=True)
# strip up to </think>, then json.loads

Downloads last month: 124

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for kstorm77/quick-add-qwen3-0.6b

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

(317)

this model