Instructions to use grapeV-ai/Qwen3.5-122B-A10B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use grapeV-ai/Qwen3.5-122B-A10B-GGUF 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 grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF: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 grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF: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 grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use grapeV-ai/Qwen3.5-122B-A10B-GGUF with Ollama:
```
ollama run hf.co/grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use grapeV-ai/Qwen3.5-122B-A10B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF: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": "grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use grapeV-ai/Qwen3.5-122B-A10B-GGUF with Docker Model Runner:
```
docker model run hf.co/grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M
```

Lemonade

How to use grapeV-ai/Qwen3.5-122B-A10B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use grapeV-ai/Qwen3.5-122B-A10B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF: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 grapeV-ai/Qwen3.5-122B-A10B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use grapeV-ai/Qwen3.5-122B-A10B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf grapeV-ai/Qwen3.5-122B-A10B-GGUF: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 "grapeV-ai/Qwen3.5-122B-A10B-GGUF: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"

What is this?

Alibaba Cloudの思考＆非思考ハイブリッド型MoEモデルQwen3.5-122B-A10BをGGUFフォーマットに変換したものです。
非思考モードの需要が高いことを鑑み、デフォルトでは非思考モデルとしてふるまうようにchat_templateを変更しています。

imatrix dataset

日本語能力を重視し、日本語が多量に含まれるTFMC/imatrix-dataset-for-japanese-llmデータセットを使用しました。

Quants

各クオンツのベンチマーク測定結果（API版Gemma3 27B採点によるElyza_tasks 100）をまとめておきます。

思考なし

クオンツ	スコア	コメント
Q8_0	4.59
Q6_K	4.6
Q5_K_M	4.58
Q4_K_M	4.535
IQ4_XS	4.71	推奨
MXFP4_MOE	4.62

思考あり

調査中

Note

llama.cpp-b8226以前と合わせてのご利用を推奨します。
なぜなら、llama.cpp-b8227以降はチャットテンプレートを無視して思考を開始させてしまうからです。

また、-mm mmproj-Qwen3.5-122b-BF16.ggufでビジョンエンコーダーをロードし、Vision対応モデルとして使用することができます。

Environment

Windows版llama.cpp-b8226およびllama.cpp-b8145同時リリースのconvert-hf-to-gguf.pyを使用して量子化作業を実施しました。

License

Apache 2.0

Developer

Alibaba Cloud

Downloads last month: 139

GGUF

Model size

122B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

View +1 variant

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