Instructions to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M 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 momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M: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 momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M: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 momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M

LM Studio
Jan
Ollama
How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M with Ollama:
```
ollama run hf.co/momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M
```

Unsloth Studio

How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M 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 momijiina/Qwen3.8-22.62b-v3-Q4_K_M 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 momijiina/Qwen3.8-22.62b-v3-Q4_K_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for momijiina/Qwen3.8-22.62b-v3-Q4_K_M to start chatting

How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M: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": "momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M
```

Lemonade

How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-22.62b-v3-Q4_K_M-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M: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 momijiina/Qwen3.8-22.62b-v3-Q4_K_M:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use momijiina/Qwen3.8-22.62b-v3-Q4_K_M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf momijiina/Qwen3.8-22.62b-v3-Q4_K_M: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 "momijiina/Qwen3.8-22.62b-v3-Q4_K_M: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"

qwen3.8-22.62b-v3

Qwen3.8 27Bをベースに、コーディング・会話・tool callingの維持を目的として構造を縮小化したテキスト専用モデルです。
64 layers、hidden size 5,120、vocabulary 248,320を維持し、FFNを17,408から13,056へ変更しました。
元27B top-100+tail pure forward KDを用い、step 900でpauseしたcp900を量子化しています。

V2はお蔵入りとしv3に修正しました。
V1からはかなり調整しています。
V4,V5の試験は現在、調整中
なぜかThinking終了後もThinkingのような挙動をすることあり？(調査中)
V1にあった継続思考力の低下は解決済

Parameters: 22.62B
Layers: 64
Format: GGUF Q4_K_M
Size: 13,957,089,952 bytes（12.999 GiB）
Vision非対応&MTPなし&text-only
851 tensors / 22,617,808,384 parameters
一般会話、Webデザイン、基本コード、tool callingは概ね動作します。
元モデル由来のハルシネーションや曖昧な知識が残っています。
Thinking ON/OFFの挙動はLM Studioなど実行環境の実装に依存します。(初期だとExtraHighかも)

利用条件は元モデルのライセンスに従ってください。

Downloads last month: 80

GGUF

Model size

23B params

Architecture

qwen35

Hardware compatibility

4-bit

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