Feature Extraction
Transformers
Safetensors
GGUF
English
Korean
qwen3_5_text
qwen3.5
backbone
headless
classification-backbone
knowledge-distillation
model-compression
vocabulary-pruning
korean
edge-ai
conversational
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
- Transformers
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
- Pi
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"
| { | |
| "schema_version": "standalone4l-v128k-headless-base-v1", | |
| "source_headless_base": "results/standalone4l_base/20260731-v1/huggingface_release_v3", | |
| "source_128k_classifier_tokenizer": "results/standalone4l_base/20260731-v1/huggingface_release_v3/models/semeval-propaganda", | |
| "source_base_config_sha256": "bb33f3a52d787e15db7d516c6de36a7ddd2c675e7964d5b9309131c4e5d365f0", | |
| "source_base_weights_sha256": "2732c616772fe320cdea228ab4554981418b1b2bf615c4183fb1ac8e6e2168d3", | |
| "source_v128_tokenizer_sha256": "dd2b27bbd4f5f650c7200300013ef03ee01efe620b128343163521bf04806fc8", | |
| "vocab_mapping_sha256": "7dbc45107c169d43d9ddb187cda7d5b1f69c1d1c3ed3db088f83e3c08737a85f", | |
| "old_vocab_size": 248320, | |
| "new_vocab_size": 128000, | |
| "mapping_rule": "v128 tokenizer token-string -> full tokenizer token-string -> source embedding row", | |
| "removed_tensor": "score.weight absent; source is headless", | |
| "output_weights_sha256": "7aa658a1adef9665fa86c0cdd97d0e32c57e3dd6be618eaaf7794448930d387c", | |
| "output_weight_bytes": 421783648, | |
| "output_parameter_count": 210889024 | |
| } | |