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"

mp-juuuns commited on about 8 hours ago

Commit

86593fd

verified ·

1 Parent(s): b3f0548

Restore the spec table's separator row and resync SHA256SUMS

Browse files

The |---|---| separator under the spec table at the top of README.md was lost in an edit, so the table rendered as plain text. Restored.

Kept the other change from that edit: the duplicated horizontal rule before 'The final test' was mine and removing it was right. README_KO.md had the same duplicate and now matches.

SHA256SUMS had gone stale for README.md, since a web edit does not regenerate it. Both entries are correct again; all 362 verify.

Files changed (3) hide show

README.md +1 -0
README_KO.md +0 -2
SHA256SUMS +2 -2

README.md CHANGED Viewed

@@ -37,6 +37,7 @@ arguing that this model is better than anything. We built it, we measured it bes
 models, and where we have a guess about why a number moved we say it is a guess.
 | | |
 | **Root model** | 4 layers, hidden 1024, **39,866** vocabulary entries |
 | **Parameters** | 120,639,808 |
 | **BF16 weights** | 241,285,160 bytes (230.1 MiB) |

 models, and where we have a guess about why a number moved we say it is a guess.
 | | |
+|---|---|
 | **Root model** | 4 layers, hidden 1024, **39,866** vocabulary entries |
 | **Parameters** | 120,639,808 |
 | **BF16 weights** | 241,285,160 bytes (230.1 MiB) |

README_KO.md CHANGED Viewed

@@ -538,8 +538,6 @@ arm이 최고점으로부터 그 한 개 SD 안에 들어 있습니다. 몇몇 S
 ---
----
 ## 최종 테스트
 2026-09-23에 SemEval-2020 Task 11 **v2 테스트 재분할**을 대상으로 고정 프로토콜 평가를 한 번

 ---
 ## 최종 테스트
 2026-09-23에 SemEval-2020 Task 11 **v2 테스트 재분할**을 대상으로 고정 프로토콜 평가를 한 번

SHA256SUMS CHANGED Viewed

@@ -1,6 +1,6 @@
 77fd4710def9ec3c0f6225800e0235f15a425abd4a8b03559127fcd782612049  LICENSE
-92ea56cfedc17d67ad2dcd4fa3d40378ac6a8ed27456bb24721284030e3349b3  README.md
-a62448e225d5b10ec7a8bc998be7cc7db209ba59f1ff6ce66e4b2293ddbaa2b6  README_KO.md
 bf4073de38c3905effd2dcce6f38894f325c8a0a79bedfb2779f0545c86b9c9f  benchmark/BENCHMARK_CARD.md
 e01bd7ac59a3212e44b77e5fcfbc7c41ec91d74c998306d2438209d6ef1a3aa7  benchmark/arm_summary.csv
 2c544c3907c4f592d724f658850b8c6b9634845cb5b50a66ebd2ea6c5d48ce0b  benchmark/compression_ladder.csv

 77fd4710def9ec3c0f6225800e0235f15a425abd4a8b03559127fcd782612049  LICENSE
+272d60fd3c4f3605eedc038856fd39c5778cf67a01b6422982eb679a9d65b2fb  README.md
+93dfd18112f9770b7bbd6910df9bc3169df137aeb7b7d1a020e070dfea3c5109  README_KO.md
 bf4073de38c3905effd2dcce6f38894f325c8a0a79bedfb2779f0545c86b9c9f  benchmark/BENCHMARK_CARD.md
 e01bd7ac59a3212e44b77e5fcfbc7c41ec91d74c998306d2438209d6ef1a3aa7  benchmark/arm_summary.csv
 2c544c3907c4f592d724f658850b8c6b9634845cb5b50a66ebd2ea6c5d48ce0b  benchmark/compression_ladder.csv