Instructions to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Transformers:

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

pipe = pipeline("feature-extraction", model="mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless")
model = AutoModel.from_pretrained("mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless 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 mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
llama cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
llama cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

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 mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

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 mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Use Docker

docker model run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

LM Studio
Jan
Ollama
How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Ollama:
```
ollama run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
```
Unsloth Desktop

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

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": "mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Docker Model Runner:
```
docker model run hf.co/mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0
```

Lemonade

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Run and chat with the model

lemonade run user.qwen3.5-4l-vocab40k-en-ko-headless-Q8_0

List all available models

lemonade list

Hermes Agent

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

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 mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0

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 "mp-juuuns/qwen3.5-4l-vocab40k-en-ko-headless:Q8_0" \
  --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"

qwen3.5-4l-vocab40k-en-ko-headless / benchmark /figures /02_per_label_transfer_metrics.csv

mp-juuuns

Add Matplotlib benchmark visualizations and source data

7b8cd30 verified about 2 months ago

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	label,support,precision_mean,recall_mean,f1_mean
	Thought-terminating_Cliches,7,0.3492063492063492,0.3333333333333333,0.319014319014319
	"Whataboutism,Straw_Men,Red_Herring",10,0.2761904761904762,0.5,0.330448343079922
	Black-and-White_Fallacy,12,0.3667929292929293,0.47222222222222227,0.4053892966936445
	Appeal_to_Authority,11,0.3958614484930274,0.5757575757575757,0.4657088122605364
	"Exaggeration,Minimisation",22,0.4620811287477954,0.7727272727272727,0.5727040816326531
	Slogans,14,0.5161361626878869,0.7380952380952381,0.5834824090638044
	"Bandwagon,Reductio_ad_hitlerum",7,0.4851851851851852,0.8095238095238095,0.6006778309409889
	Repetition,23,0.5196620583717357,0.7536231884057971,0.6147520781519571
	Appeal_to_fear-prejudice,24,0.5540244550770866,0.7361111111111112,0.6277521761392729
	Causal_Oversimplification,18,0.5428824049513704,0.7962962962962963,0.6446992723588468
	Flag-Waving,17,0.5642642642642642,0.8235294117647058,0.6464506172839506
	Doubt,22,0.5223832528180354,0.9848484848484849,0.6802057822343391
	"Name_Calling,Labeling",33,0.7380952380952381,0.9090909090909091,0.8125087983911513
	Loaded_Language,45,0.8649032907793016,0.9851851851851852,0.9208741623431562