Instructions to use Goldkoron/Qwen3.5-397B-A17B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Goldkoron/Qwen3.5-397B-A17B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Goldkoron/Qwen3.5-397B-A17B",
	filename="Qwen3.5-397B-A17B-K_G_1.95.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Goldkoron/Qwen3.5-397B-A17B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Goldkoron/Qwen3.5-397B-A17B
# Run inference directly in the terminal:
llama-cli -hf Goldkoron/Qwen3.5-397B-A17B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Goldkoron/Qwen3.5-397B-A17B
# Run inference directly in the terminal:
llama-cli -hf Goldkoron/Qwen3.5-397B-A17B

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 Goldkoron/Qwen3.5-397B-A17B
# Run inference directly in the terminal:
./llama-cli -hf Goldkoron/Qwen3.5-397B-A17B

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 Goldkoron/Qwen3.5-397B-A17B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Goldkoron/Qwen3.5-397B-A17B

Use Docker

docker model run hf.co/Goldkoron/Qwen3.5-397B-A17B

LM Studio
Jan
Ollama
How to use Goldkoron/Qwen3.5-397B-A17B with Ollama:
```
ollama run hf.co/Goldkoron/Qwen3.5-397B-A17B
```

Unsloth Studio

How to use Goldkoron/Qwen3.5-397B-A17B 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 Goldkoron/Qwen3.5-397B-A17B 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 Goldkoron/Qwen3.5-397B-A17B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Goldkoron/Qwen3.5-397B-A17B to start chatting

How to use Goldkoron/Qwen3.5-397B-A17B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Goldkoron/Qwen3.5-397B-A17B

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Goldkoron/Qwen3.5-397B-A17B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Goldkoron/Qwen3.5-397B-A17B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Goldkoron/Qwen3.5-397B-A17B

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 Goldkoron/Qwen3.5-397B-A17B

Run Hermes

hermes

Docker Model Runner
How to use Goldkoron/Qwen3.5-397B-A17B with Docker Model Runner:
```
docker model run hf.co/Goldkoron/Qwen3.5-397B-A17B
```

Lemonade

How to use Goldkoron/Qwen3.5-397B-A17B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Goldkoron/Qwen3.5-397B-A17B

Run and chat with the model

lemonade run user.Qwen3.5-397B-A17B-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-397B-A17B — Gutenberg Quants

Quantizations of Qwen3.5-397B-A17B using the Gutenberg (Q_K_G) quantization strategy.

Available Quants

Quant	Size	BPW	Mean KLD	Same Top P
K_G_4.00	184.5 GiB	4.00	0.021106	92.838%
K_G_2.93	135.7 GiB	2.93	0.030123	91.177%
K_G_2.50	115.6 GiB	2.50	0.035966	90.618%
K_G_2.25	103.9 GiB	2.25	0.047857	89.360%
K_G_1.95	89.8 GiB	1.95	0.071636	86.940%

KLD and Same Top P measured against Q8_0 reference logits (8192 context, 10 chunks).

Why Gutenberg?

Standard quantization (K_M) applies uniform rules to all tensors. Gutenberg uses KLD sensitivity data to allocate precision where it should matter most, upgrading the tensors that have the highest measured impact on output quality while keeping less important tensors at the base level. Non-expert tensors are kept at Q8_0 for their disproportionate quality impact.

The result is significantly better quality than standard quants at the same model size (on paper).

Compatibility

Fully compatible with stock llama.cpp, llama-server, LM Studio, and any GGUF-compatible runtime. No custom builds required.

Downloads last month: 327

GGUF

Model size

396B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for Goldkoron/Qwen3.5-397B-A17B

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